Mobile website
- Latest News
- Recommended
- Multi-dimensional Container Monitoring System for Kubernetes
- Double-auction-based Task Offloading and Resource Allocation Strategy for Mobile Edge Computing
- Wafer Defect Detection Based on Separable Convolution and Attention Mechanism
- Method of Enhancing Relief Shading for Large-scale Battlefield
- Attention Detection Based on Symmetrical Dual-channel EEG Signals
- ADS-B Anomaly Detection Based on Attention Mechanism for CNN-LSTM
- Research on Optimization Technology for Container Live Migration More+
- Current Issue
- Online First
- Archive
- Click Rank
- Most Downloaded
- 综述文章
Article Search
Search by issue
Select AllDeselectExport
Display Method:
2023,32(5):1-10, DOI: 10.15888/j.cnki.csa.009086
Abstract:
Lack of concentration is an attention disorder that is common among teenagers, and it directly affects people’s learning and work efficiency. Most of the traditional attention detection methods rely on the observation of expressions, postures, and other behaviors and fail to objectively and accurately reflect attention states. Amid the rapid development of physiological detection technology, attention detection based on electroencephalography (EEG) signals has received considerable attention recently. However, related studies still have the problem of low detection accuracy. In this study, the EEG signals of 155 college students in the three states of being focused, distracted, and relaxed are collected, and the three attention states are identified by various machine learning methods on the basis of the wavelet features, differential entropy features and power spectrum features of the signals. The results show that these features of EEG signals can effectively distinguish the attention states of the subjects. The average accuracy of the detection method based on symmetrical dual-channel features is (80.84±3)%, and the detection precision of this method is significantly higher than that of the method based on single-channel features.
Lack of concentration is an attention disorder that is common among teenagers, and it directly affects people’s learning and work efficiency. Most of the traditional attention detection methods rely on the observation of expressions, postures, and other behaviors and fail to objectively and accurately reflect attention states. Amid the rapid development of physiological detection technology, attention detection based on electroencephalography (EEG) signals has received considerable attention recently. However, related studies still have the problem of low detection accuracy. In this study, the EEG signals of 155 college students in the three states of being focused, distracted, and relaxed are collected, and the three attention states are identified by various machine learning methods on the basis of the wavelet features, differential entropy features and power spectrum features of the signals. The results show that these features of EEG signals can effectively distinguish the attention states of the subjects. The average accuracy of the detection method based on symmetrical dual-channel features is (80.84±3)%, and the detection precision of this method is significantly higher than that of the method based on single-channel features.
2023,32(5):11-19, DOI: 10.15888/j.cnki.csa.009063
Abstract:
Relief shading is an important part of large-scale battlefield simulation. Aiming at the problem that texture features of the existing relief shading technologies are not obvious in terms of details, this study proposes a large-scale battlefield relief shading enhancement method that combines elevation curvature and ambient occlusion. In the first step, by analyzing the curvature attribute of digital elevation data, a terrain curvature map is generated and then superimposed with satellite images to highlight geomorphic feature lines. In the second step, an ambient occlusion calculation method based on depthwise separable convolution is proposed, which can enhance the visual performance of battlefield terrain in gullies. Finally, the curvature map, ambient occlusion, and satellite images are fused to generate a real-time relief shading effect. Experiments show that the proposed method can present better visual effects on low-level global satellite images so that the observer can further analyze the texture features in terms of terrain details while grasping the overall trend of the three-dimensional terrain.
Relief shading is an important part of large-scale battlefield simulation. Aiming at the problem that texture features of the existing relief shading technologies are not obvious in terms of details, this study proposes a large-scale battlefield relief shading enhancement method that combines elevation curvature and ambient occlusion. In the first step, by analyzing the curvature attribute of digital elevation data, a terrain curvature map is generated and then superimposed with satellite images to highlight geomorphic feature lines. In the second step, an ambient occlusion calculation method based on depthwise separable convolution is proposed, which can enhance the visual performance of battlefield terrain in gullies. Finally, the curvature map, ambient occlusion, and satellite images are fused to generate a real-time relief shading effect. Experiments show that the proposed method can present better visual effects on low-level global satellite images so that the observer can further analyze the texture features in terms of terrain details while grasping the overall trend of the three-dimensional terrain.
2023,32(5):20-27, DOI: 10.15888/j.cnki.csa.009062
Abstract:
A lightweight network based on depthwise separable convolution and the attention mechanism is proposed for fast detection of surface defects on semiconductor wafers, and experiments are conducted on the WM-811K dataset. As the proportions of defects of nine different categories in this dataset are imbalanced, a data enhancement method is used to expand the data for defect categories with few data. The depthwise separable convolution in this model can reduce the number of parameters and improve the inference speed of the model. The attention mechanism can make the model pay more attention to the defective regions in the wafer image so that the model can achieve better classification results. The experiments show that the average accuracy of the proposed method on the WM-811K dataset is as high as 96.5%, which is improved to varying degrees compared with that of ANN, VGG16, and MobileNetv2. In addition, the number of parameters and the amount of operation are only 73.5% and 28.6% of those of the classical lightweight network MobileNetv2, respectively.
A lightweight network based on depthwise separable convolution and the attention mechanism is proposed for fast detection of surface defects on semiconductor wafers, and experiments are conducted on the WM-811K dataset. As the proportions of defects of nine different categories in this dataset are imbalanced, a data enhancement method is used to expand the data for defect categories with few data. The depthwise separable convolution in this model can reduce the number of parameters and improve the inference speed of the model. The attention mechanism can make the model pay more attention to the defective regions in the wafer image so that the model can achieve better classification results. The experiments show that the average accuracy of the proposed method on the WM-811K dataset is as high as 96.5%, which is improved to varying degrees compared with that of ANN, VGG16, and MobileNetv2. In addition, the number of parameters and the amount of operation are only 73.5% and 28.6% of those of the classical lightweight network MobileNetv2, respectively.
2023,32(5):28-35, DOI: 10.15888/j.cnki.csa.009120
Abstract:
In recent years, researchers have found that the hyperspectral image classification method based on dual branch structure can more effectively extract the spectral and spatial features of the image for classification. However, in the dual branch structure, each branch only focuses on refining and extracting spectral or spatial features, with the study on cross-dimensional spectral-spatial feature interaction ignored, and the partial interaction extracted by the two branches respectively is not obvious, which affects the performance of classification. To solve this problem, this study proposes a hyperspectral image classification method based on global attention information interaction. First, the dense connection network is used to divide the image into two branches to refine the spectral and spatial features, respectively, and then the channel global attention features and spatial global attention features are obtained by combining the global attention mechanism (GAM). Finally, an information interaction module is used to realize the interaction of spectral and spatial information, which makes full use of spectral and spatial information to achieve classification. The method proposed in this study has been tested on Pavia University (PU) and Salinas Valley (SV) datasets, respectively. Compared with that of the other four methods, the classification performance of the method proposed in this study is significantly improved.
In recent years, researchers have found that the hyperspectral image classification method based on dual branch structure can more effectively extract the spectral and spatial features of the image for classification. However, in the dual branch structure, each branch only focuses on refining and extracting spectral or spatial features, with the study on cross-dimensional spectral-spatial feature interaction ignored, and the partial interaction extracted by the two branches respectively is not obvious, which affects the performance of classification. To solve this problem, this study proposes a hyperspectral image classification method based on global attention information interaction. First, the dense connection network is used to divide the image into two branches to refine the spectral and spatial features, respectively, and then the channel global attention features and spatial global attention features are obtained by combining the global attention mechanism (GAM). Finally, an information interaction module is used to realize the interaction of spectral and spatial information, which makes full use of spectral and spatial information to achieve classification. The method proposed in this study has been tested on Pavia University (PU) and Salinas Valley (SV) datasets, respectively. Compared with that of the other four methods, the classification performance of the method proposed in this study is significantly improved.
2023,32(5):36-44, DOI: 10.15888/j.cnki.csa.009055
Abstract:
When the artificial potential field method is employed for unmanned aerial vehicle (UAV) path planning, there are often some problems, such as unreachable targets, repeated motion trajectories, and large path lengths. The traditional artificial potential field method fails to adjust the repulsion coefficient according to the specific information of the environment, while the existing improved methods cannot take into account the planning effect and planning time while adaptively adjusting the repulsion coefficient. To solve the above problems, this study proposes a UAV path planning method based on the adaptive repulsion coefficient with the help of deep learning. Firstly, the most suitable repulsion coefficient sample set in a specific environment is found by integrating a genetic algorithm and the artificial potential field method. Secondly, a residual neural network is trained with the sample set. Finally, the repulsion coefficient adapted to the environment is calculated by the residual neural network, and then the artificial potential field method is used for path planning. Simulation experiments show that the proposed method solves the abovementioned problems in path planning with the artificial potential field method to a certain extent. It has excellent performance in planning effect and planning time and can well meet the requirements for current environment adaptation and rapid planning in UAV path planning.
When the artificial potential field method is employed for unmanned aerial vehicle (UAV) path planning, there are often some problems, such as unreachable targets, repeated motion trajectories, and large path lengths. The traditional artificial potential field method fails to adjust the repulsion coefficient according to the specific information of the environment, while the existing improved methods cannot take into account the planning effect and planning time while adaptively adjusting the repulsion coefficient. To solve the above problems, this study proposes a UAV path planning method based on the adaptive repulsion coefficient with the help of deep learning. Firstly, the most suitable repulsion coefficient sample set in a specific environment is found by integrating a genetic algorithm and the artificial potential field method. Secondly, a residual neural network is trained with the sample set. Finally, the repulsion coefficient adapted to the environment is calculated by the residual neural network, and then the artificial potential field method is used for path planning. Simulation experiments show that the proposed method solves the abovementioned problems in path planning with the artificial potential field method to a certain extent. It has excellent performance in planning effect and planning time and can well meet the requirements for current environment adaptation and rapid planning in UAV path planning.
2023,32(5):45-56, DOI: 10.15888/j.cnki.csa.009110
Abstract:
In the mobile edge computing (MEC) system, users’ offloading strategies will affect energy consumption and computing cost, which in turn affects the users’ benefit. However, most of the existing studies have not considered the impact of users’ offloading strategies and resource request strategies on the benefit in the random distribution of edge servers. Therefore, this study proposes a computing offloading and resource allocation strategy based on an improved double auction algorithm. Firstly, this strategy models the interaction process between users and edge servers as a Stackelberg game and proves that there is a unique Nash equilibrium point in the game. Secondly, the users’ willingness to offload different servers and the amount of computing resource requests are calculated, and then users and the optimal server are auctioned. Finally, the traversal method is employed to exchange some transactions in the previous auction for the optimal overall benefit of the system. Simulation results show that, compared with other benchmark algorithms, the proposed algorithm can improve the total benefit of system users by 33.4% in the scenario of random distribution of servers and effectively reduce system loss.
In the mobile edge computing (MEC) system, users’ offloading strategies will affect energy consumption and computing cost, which in turn affects the users’ benefit. However, most of the existing studies have not considered the impact of users’ offloading strategies and resource request strategies on the benefit in the random distribution of edge servers. Therefore, this study proposes a computing offloading and resource allocation strategy based on an improved double auction algorithm. Firstly, this strategy models the interaction process between users and edge servers as a Stackelberg game and proves that there is a unique Nash equilibrium point in the game. Secondly, the users’ willingness to offload different servers and the amount of computing resource requests are calculated, and then users and the optimal server are auctioned. Finally, the traversal method is employed to exchange some transactions in the previous auction for the optimal overall benefit of the system. Simulation results show that, compared with other benchmark algorithms, the proposed algorithm can improve the total benefit of system users by 33.4% in the scenario of random distribution of servers and effectively reduce system loss.
Article Search
Search by issue
Select AllDeselectExport
Display Method:
Available online: May 24, 2023 , DOI: 10.15888/j.cnki.csa.009159
Abstract:
Graph neural networks (GNNs) have attracted widespread attention due to their powerful modeling capabilities, and they are often used to solve node classification tasks on graphs. At this stage, the commonly used model with the graph convolution network (GCN) as the core solves such problems. However, due to over-fitting and over-smoothing, the deep node embedding representation effect is not positive. Therefore, this study proposes a graph convolutional neural residual networks (GCNRN) model that combines residual connection and self-attention based on GCN kernel to improve the generalization ability of GCN. At the same time, in order to integrate more in-depth information, this study introduces a fusion mechanism, uses fuzzy integral to fuse multiple classifiers, and finally improves the model testing accuracy. In order to verify the superiority of the proposed method, this study uses OGB-arxiv and commonly used citation datasets to conduct comparative experiments. Compared with many existing models with GCN as the core, the GCNRN model has an average improvement of node classification accuracy by 2% and avoids the traditional over-fitting and over-smoothing phenomena. In addition, the experimental results show that the multi-classifier model with the fusion module based on fuzzy integral has a better classification effect than the traditional fusion method.
Graph neural networks (GNNs) have attracted widespread attention due to their powerful modeling capabilities, and they are often used to solve node classification tasks on graphs. At this stage, the commonly used model with the graph convolution network (GCN) as the core solves such problems. However, due to over-fitting and over-smoothing, the deep node embedding representation effect is not positive. Therefore, this study proposes a graph convolutional neural residual networks (GCNRN) model that combines residual connection and self-attention based on GCN kernel to improve the generalization ability of GCN. At the same time, in order to integrate more in-depth information, this study introduces a fusion mechanism, uses fuzzy integral to fuse multiple classifiers, and finally improves the model testing accuracy. In order to verify the superiority of the proposed method, this study uses OGB-arxiv and commonly used citation datasets to conduct comparative experiments. Compared with many existing models with GCN as the core, the GCNRN model has an average improvement of node classification accuracy by 2% and avoids the traditional over-fitting and over-smoothing phenomena. In addition, the experimental results show that the multi-classifier model with the fusion module based on fuzzy integral has a better classification effect than the traditional fusion method.
Available online: May 24, 2023 , DOI: 10.15888/j.cnki.csa.009180
Abstract:
As cloud computing rapidly develops, container technology, represented by Docker, has been gradually paid attention to. At present, three common container orchestration tools are Kubernetes, Docker Swarm, and Rancher. However, when the total capacity of all working nodes exceeds the limit, the existing container orchestration tools will have problems such as long response time and large resource occupation. Therefore, the least space unused (LSD) algorithm and least recently used and space unused (LRU-SD) algorithm are designed in this study and applied to three kinds of orchestration tools. When the total capacity exceeds the upper limit, the non-working nodes are deleted and new working nodes are added. In practice, the LSD algorithm deletes the working node with the least remaining space, while the LRU-SD algorithm first considers deleting the longest unused node. When there are multiple qualified nodes, the working node with the least remaining space is deleted. In the experiment part, the impacts of different algorithms on three container orchestration tools are analyzed and compared in terms of response time, CPU, and memory. The experimental results show that the LSD algorithm, the LRU-SD algorithm, and the LRU algorithm can not only improve the response time of the orchestration tools but also increase the utilization of resources. At the same time, the LRU-SD algorithm is the most effective in improving CPU utilization.
As cloud computing rapidly develops, container technology, represented by Docker, has been gradually paid attention to. At present, three common container orchestration tools are Kubernetes, Docker Swarm, and Rancher. However, when the total capacity of all working nodes exceeds the limit, the existing container orchestration tools will have problems such as long response time and large resource occupation. Therefore, the least space unused (LSD) algorithm and least recently used and space unused (LRU-SD) algorithm are designed in this study and applied to three kinds of orchestration tools. When the total capacity exceeds the upper limit, the non-working nodes are deleted and new working nodes are added. In practice, the LSD algorithm deletes the working node with the least remaining space, while the LRU-SD algorithm first considers deleting the longest unused node. When there are multiple qualified nodes, the working node with the least remaining space is deleted. In the experiment part, the impacts of different algorithms on three container orchestration tools are analyzed and compared in terms of response time, CPU, and memory. The experimental results show that the LSD algorithm, the LRU-SD algorithm, and the LRU algorithm can not only improve the response time of the orchestration tools but also increase the utilization of resources. At the same time, the LRU-SD algorithm is the most effective in improving CPU utilization.
Available online: May 24, 2023 , DOI: 10.15888/j.cnki.csa.009186
Abstract:
Hyperspectral images have multiple bands and a strong correlation between bands, but their spatial texture and geometric information are poorly expressed. The traditional classification model has insufficient extraction of spatial spectral features and large calculation, and its classification performance needs to be improved. To solve this problem, a multi-scale and multi-resolution attention feature fusion convolution network (WTCAN) based on the wavelet transform is proposed. The concept of wavelet transform is applied to decompose the spectral band four times, and the hierarchical extraction of spectral features can reduce the calculation amount. The network has designed the spatial information extraction module and introduced the pyramid attention mechanism. By designing the reverse jump connection network structure, it uses multiple scales to obtain the spatial position features and enhances the expression ability of spatial texture, which can effectively improve the defects of traditional 2D-CNN feature extraction, such as single scale and the ignoring of spatial texture details. The proposed WTCAN model is experimented on the hyperspectral datasets with different spatial resolutions—Indian Pines (IP), WHU_Hi_HanChuan (HanChuan), and WHU_ Hi_ HongHu (HongHu) repectively. By comparing the effects of SVM, 2D-CNN, DBMA, DBDA, and HybridSN models, the WTCAN model achieves excellent classification results. The overall classification precision of the three datasets reaches 98.41%, 99.64%, and 99.67% respectively, which can provide a valuable reference for the research on the classification of hyperspectral images.
Hyperspectral images have multiple bands and a strong correlation between bands, but their spatial texture and geometric information are poorly expressed. The traditional classification model has insufficient extraction of spatial spectral features and large calculation, and its classification performance needs to be improved. To solve this problem, a multi-scale and multi-resolution attention feature fusion convolution network (WTCAN) based on the wavelet transform is proposed. The concept of wavelet transform is applied to decompose the spectral band four times, and the hierarchical extraction of spectral features can reduce the calculation amount. The network has designed the spatial information extraction module and introduced the pyramid attention mechanism. By designing the reverse jump connection network structure, it uses multiple scales to obtain the spatial position features and enhances the expression ability of spatial texture, which can effectively improve the defects of traditional 2D-CNN feature extraction, such as single scale and the ignoring of spatial texture details. The proposed WTCAN model is experimented on the hyperspectral datasets with different spatial resolutions—Indian Pines (IP), WHU_Hi_HanChuan (HanChuan), and WHU_ Hi_ HongHu (HongHu) repectively. By comparing the effects of SVM, 2D-CNN, DBMA, DBDA, and HybridSN models, the WTCAN model achieves excellent classification results. The overall classification precision of the three datasets reaches 98.41%, 99.64%, and 99.67% respectively, which can provide a valuable reference for the research on the classification of hyperspectral images.
Available online: May 24, 2023 , DOI: 10.15888/j.cnki.csa.009193
Abstract:
Currently, the physiological signals in the classification of acrophobia emotions mainly involve electroencephalogram (EEG), electrocardiogram (ECG), and skin electromyography (EMG). However, due to the limitations of EEG acquisition and processing as well as the fusion between multimodal signals, a dynamic weighted decision fusion algorithm based on six peripheral physiological signals is proposed. Firstly, the different levels of acrophobia are induced in the subjects through the virtual reality technology, while six peripheral physiological signals (ECG, BVP, EMG, EDA, SKT, and RESP) are recorded. Secondly, the statistical and event-related features of the signals are extracted to construct a dataset of acrophobia emotions. Thirdly, a dynamic weighted decision fusion algorithm is proposed according to the classification performance, modal, and cross-modal information, so as to effectively integrate multi-modal signals to improve the recognition precision. Finally, the experimental results are compared with previous relevant research, and then verified on the open-source WESAD emotion dataset. The conclusions show that multi-modal peripheral physiological signals are conducive to enhancing the classification performance of acrophobia emotions, and the proposed dynamic weighted decision fusion algorithm significantly improves both the classification performance and model robustness.
Currently, the physiological signals in the classification of acrophobia emotions mainly involve electroencephalogram (EEG), electrocardiogram (ECG), and skin electromyography (EMG). However, due to the limitations of EEG acquisition and processing as well as the fusion between multimodal signals, a dynamic weighted decision fusion algorithm based on six peripheral physiological signals is proposed. Firstly, the different levels of acrophobia are induced in the subjects through the virtual reality technology, while six peripheral physiological signals (ECG, BVP, EMG, EDA, SKT, and RESP) are recorded. Secondly, the statistical and event-related features of the signals are extracted to construct a dataset of acrophobia emotions. Thirdly, a dynamic weighted decision fusion algorithm is proposed according to the classification performance, modal, and cross-modal information, so as to effectively integrate multi-modal signals to improve the recognition precision. Finally, the experimental results are compared with previous relevant research, and then verified on the open-source WESAD emotion dataset. The conclusions show that multi-modal peripheral physiological signals are conducive to enhancing the classification performance of acrophobia emotions, and the proposed dynamic weighted decision fusion algorithm significantly improves both the classification performance and model robustness.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009199
Abstract:
Dockerfile defines a set of instructions for building container images, which instruct how the containerized applications should be built. Recent studies have shown that there are quite a lot of quality problems in Dockerfile. This study proposes a new tool, namely Dockerfile Miner (DMiner) to extract implicit rules from high-quality Dockerfile, and these rules will help to improve the quality of Dockerfile. DMiner is mainly divided into three modules, which are responsible for the collection and filtering of Dockerfile, parsing of Dockerfile, and mining and extraction of Dockerfile rules. DMiner parses Dockerfile into a unified sequential representation and uses a sequential rule mining algorithm to extract rules. This tool expands the existing Dockerfile dataset and extracts nine new rules that have not appeared in other work. A large number of experiments on real datasets show that the tool is effective and efficient.
Dockerfile defines a set of instructions for building container images, which instruct how the containerized applications should be built. Recent studies have shown that there are quite a lot of quality problems in Dockerfile. This study proposes a new tool, namely Dockerfile Miner (DMiner) to extract implicit rules from high-quality Dockerfile, and these rules will help to improve the quality of Dockerfile. DMiner is mainly divided into three modules, which are responsible for the collection and filtering of Dockerfile, parsing of Dockerfile, and mining and extraction of Dockerfile rules. DMiner parses Dockerfile into a unified sequential representation and uses a sequential rule mining algorithm to extract rules. This tool expands the existing Dockerfile dataset and extracts nine new rules that have not appeared in other work. A large number of experiments on real datasets show that the tool is effective and efficient.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009207
Abstract:
Open-sourced datasets accelerate the development of deep learning, while unauthorized data usage frequently happens. To protect the dataset copyright, this study proposes the dataset watermarking algorithm. The watermark is embedded into the dataset before it is released. When the model is trained on this dataset, the watermark is attached to the model, which allows illegal dataset usage to be traced by verifying whether the watermark exists in a suspect model. However, existing dataset watermarking algorithms cannot provide effective and covert black-box verification under small perturbations. Given this problem, the method of embedding the watermark by a style attribute independent of the image content and label is proposed for the first time in this study, and the perturbation on the original dataset is constrained to avoid the modification of labels. The covertness and validity of the watermark are ensured without introducing the inconsistency between the image content and label or extra surrogate model. In the watermark verification stage, only the prediction results of the suspected model are applied to give the judgment via a hypothesis test. The proposed method is compared with the existing five methods on the CIFAR-10 dataset. The experimental results validate the effectiveness and fidelity of the proposed algorithm. Besides, the ablation experiments conducted in this study verify the necessity of the proposed style refinement module and the effectiveness of the proposed algorithm under various hyper-parameter settings and datasets.
Open-sourced datasets accelerate the development of deep learning, while unauthorized data usage frequently happens. To protect the dataset copyright, this study proposes the dataset watermarking algorithm. The watermark is embedded into the dataset before it is released. When the model is trained on this dataset, the watermark is attached to the model, which allows illegal dataset usage to be traced by verifying whether the watermark exists in a suspect model. However, existing dataset watermarking algorithms cannot provide effective and covert black-box verification under small perturbations. Given this problem, the method of embedding the watermark by a style attribute independent of the image content and label is proposed for the first time in this study, and the perturbation on the original dataset is constrained to avoid the modification of labels. The covertness and validity of the watermark are ensured without introducing the inconsistency between the image content and label or extra surrogate model. In the watermark verification stage, only the prediction results of the suspected model are applied to give the judgment via a hypothesis test. The proposed method is compared with the existing five methods on the CIFAR-10 dataset. The experimental results validate the effectiveness and fidelity of the proposed algorithm. Besides, the ablation experiments conducted in this study verify the necessity of the proposed style refinement module and the effectiveness of the proposed algorithm under various hyper-parameter settings and datasets.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009209
Abstract:
Given the uneven modeling degree between the user and project sides of the recommendation algorithms for knowledge graphs as well as high model complexity, a recommendation algorithm that integrates knowledge graph and lightweight graph convolutional network is proposed. On the user side, neighbor sets are generated based on user similarity, and the interaction records of users and their similar users are iteratively propagated on the knowledge graph for many times to enhance the representation of user features. On the project side, the entity on the knowledge graph is embedded and propagated to mine the project information related to user preferences. Then, the lightweight graph convolutional network is adopted to aggregate neighborhood features to obtain the feature representations of users and projects. At the same time, the attention mechanism is employed to incorporate neighborhood weights into the entities to enhance node embedding representation. Finally, the ratings between the user and the project are predicted. Experiments show that on the Book-Crossing dataset, compared with the optimal baseline, AUC and ACC are improved by 1.8% and 2.3%, respectively. On the Yelp2018 dataset, AUC and ACC are improved by 1.2% and 1.4%, respectively. The results demonstrate that the proposed model has better recommendation performance compared with other benchmark models.
Given the uneven modeling degree between the user and project sides of the recommendation algorithms for knowledge graphs as well as high model complexity, a recommendation algorithm that integrates knowledge graph and lightweight graph convolutional network is proposed. On the user side, neighbor sets are generated based on user similarity, and the interaction records of users and their similar users are iteratively propagated on the knowledge graph for many times to enhance the representation of user features. On the project side, the entity on the knowledge graph is embedded and propagated to mine the project information related to user preferences. Then, the lightweight graph convolutional network is adopted to aggregate neighborhood features to obtain the feature representations of users and projects. At the same time, the attention mechanism is employed to incorporate neighborhood weights into the entities to enhance node embedding representation. Finally, the ratings between the user and the project are predicted. Experiments show that on the Book-Crossing dataset, compared with the optimal baseline, AUC and ACC are improved by 1.8% and 2.3%, respectively. On the Yelp2018 dataset, AUC and ACC are improved by 1.2% and 1.4%, respectively. The results demonstrate that the proposed model has better recommendation performance compared with other benchmark models.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009212
Abstract:
With the gradual development of smart factories, mobile robots are applied more and more widely in the factory. However, as there are many obstacles in the factory, the traditional artificial potential field method is easy to produce unreachable targets and local minimum values and other problems. This study improves the unreachable target and the local optimal solution of the traditional artificial potential field method in path planning. Firstly, a new repulsive potential field function is adopted to solve the problem of unreachable targets by adding an influence function to the repulsive potential field function in the original artificial potential field method. Secondly, for the local optimal solution, the artificial potential field method is combined with the simulated annealing method, and the additional subpoints in the simulated annealing method are applied to break the equilibrium state, so as to get out of the obstacles. Finally, through Matlab comparison, the travel time of the proposed algorithm in 10 obstacles is improved by 6.70% and the path length is reduced by 9.20% compared with algorithms in other literature. In 20 obstacles, the travel time of the proposed algorithm is improved by 9.10% and the path length is reduced by 12.10% compared with algorithms in other literature.
With the gradual development of smart factories, mobile robots are applied more and more widely in the factory. However, as there are many obstacles in the factory, the traditional artificial potential field method is easy to produce unreachable targets and local minimum values and other problems. This study improves the unreachable target and the local optimal solution of the traditional artificial potential field method in path planning. Firstly, a new repulsive potential field function is adopted to solve the problem of unreachable targets by adding an influence function to the repulsive potential field function in the original artificial potential field method. Secondly, for the local optimal solution, the artificial potential field method is combined with the simulated annealing method, and the additional subpoints in the simulated annealing method are applied to break the equilibrium state, so as to get out of the obstacles. Finally, through Matlab comparison, the travel time of the proposed algorithm in 10 obstacles is improved by 6.70% and the path length is reduced by 9.20% compared with algorithms in other literature. In 20 obstacles, the travel time of the proposed algorithm is improved by 9.10% and the path length is reduced by 12.10% compared with algorithms in other literature.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009182
Abstract:
The design and realization of the AI scheduling engine platform based on Kubernetes is introduced in this paper. To tackle the problems of complex service configuration, the unbalanced utilization rate of computing resources of each node in the cluster and the high cost of system operation and maintenance in the current AI scheduling system, this study proposes a solution based on Kubernetes to implement container scheduling and service management. Combined with the requirements of the AI scheduling engine platform, the various modules of the platform are designed from such aspects as function implementation and platform architecture. At the same time, given the problem that Kubernetes cannot perceive GPU resources, Device Plugin is introduced to collect GPU information on each node in the cluster and report it to the scheduler. In addition, as priority algorithms in Kubernetes scheduling strategy only considers the resource utilization rate and balance degree of the node itself, disregarding the differences in the demand of different types of applications for node resources, priority algorithms based on Pearson correlation coefficient (PCC) is put forward. The scheduling of Pod is determined by calculating the complementary degree of container resources demand and node resource utilization rate, thus ensuring the resource balance of each node after the scheduling.
The design and realization of the AI scheduling engine platform based on Kubernetes is introduced in this paper. To tackle the problems of complex service configuration, the unbalanced utilization rate of computing resources of each node in the cluster and the high cost of system operation and maintenance in the current AI scheduling system, this study proposes a solution based on Kubernetes to implement container scheduling and service management. Combined with the requirements of the AI scheduling engine platform, the various modules of the platform are designed from such aspects as function implementation and platform architecture. At the same time, given the problem that Kubernetes cannot perceive GPU resources, Device Plugin is introduced to collect GPU information on each node in the cluster and report it to the scheduler. In addition, as priority algorithms in Kubernetes scheduling strategy only considers the resource utilization rate and balance degree of the node itself, disregarding the differences in the demand of different types of applications for node resources, priority algorithms based on Pearson correlation coefficient (PCC) is put forward. The scheduling of Pod is determined by calculating the complementary degree of container resources demand and node resource utilization rate, thus ensuring the resource balance of each node after the scheduling.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009184
Abstract:
The existing makeup transfer algorithms are highly effective with rich features, but they seldom take into account the scenarios of the low-resolution input images. When high-resolution images are difficult to obtain, it will be difficult for the existing makeup transfer algorithms to apply and the makeup cannot be fully transferred. In this study, a makeup transfer algorithm applied to low-resolution images is proposed, which uses the feature matrix containing makeup information as prior information and combines the super-resolution network with the makeup transfer network to produce the synergistic effect. The high-resolution makeup transfer results can be delivered even if the input image is a low-resolution one, and the robustness of postures and expressions is improved while the makeup details are fully retained. Since an end-to-end model is adopted to achieve the makeup transfer and super-resolution, a set of joint loss functions are designed, including generative adversarial loss, perceptual loss, cycle consistency loss, makeup loss, and mean square error loss functions. The proposed model attains an advanced level in both qualitative and quantitative experiments on makeup transfer and super-resolution.
The existing makeup transfer algorithms are highly effective with rich features, but they seldom take into account the scenarios of the low-resolution input images. When high-resolution images are difficult to obtain, it will be difficult for the existing makeup transfer algorithms to apply and the makeup cannot be fully transferred. In this study, a makeup transfer algorithm applied to low-resolution images is proposed, which uses the feature matrix containing makeup information as prior information and combines the super-resolution network with the makeup transfer network to produce the synergistic effect. The high-resolution makeup transfer results can be delivered even if the input image is a low-resolution one, and the robustness of postures and expressions is improved while the makeup details are fully retained. Since an end-to-end model is adopted to achieve the makeup transfer and super-resolution, a set of joint loss functions are designed, including generative adversarial loss, perceptual loss, cycle consistency loss, makeup loss, and mean square error loss functions. The proposed model attains an advanced level in both qualitative and quantitative experiments on makeup transfer and super-resolution.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009181
Abstract:
Pallet recognition and positioning is one of the critical problems in unmanned forklift trucks. At present, target detection is mostly used for pallet positioning. However, target detection can only recognize the position of the pallet in the image and cannot obtain the spatial information of the pallet. To solve this problem, this study proposes a pallet positioning method based on target and key point detection with monocular vision, which is applied to detect the pallet and calculate the current dip angle and distance of the pallet. Firstly, target detection is carried out on the pallet. Then, the image will be cropped according to the detection result and input into the key points detection network. Through the detection of the key points and the inherent geometric features of the pallet, the edge adaptive adjustment is designed to obtain the high-precision profile information of the pallet. According to the geometric constraints, a method for calculating the dip angle and distance of the pallet based on contour points is proposed, and the RANSAC algorithm is adopted to improve the precision and stability of the calculation results, thus addressing the problem of pallet positioning. Experiments indicate that the average error of the proposed algorithm is less than 5° in the calculation of dip angle and less than 110 mm in the calculation of horizontal distance. It works well for pallet positioning and is of high practical value.
Pallet recognition and positioning is one of the critical problems in unmanned forklift trucks. At present, target detection is mostly used for pallet positioning. However, target detection can only recognize the position of the pallet in the image and cannot obtain the spatial information of the pallet. To solve this problem, this study proposes a pallet positioning method based on target and key point detection with monocular vision, which is applied to detect the pallet and calculate the current dip angle and distance of the pallet. Firstly, target detection is carried out on the pallet. Then, the image will be cropped according to the detection result and input into the key points detection network. Through the detection of the key points and the inherent geometric features of the pallet, the edge adaptive adjustment is designed to obtain the high-precision profile information of the pallet. According to the geometric constraints, a method for calculating the dip angle and distance of the pallet based on contour points is proposed, and the RANSAC algorithm is adopted to improve the precision and stability of the calculation results, thus addressing the problem of pallet positioning. Experiments indicate that the average error of the proposed algorithm is less than 5° in the calculation of dip angle and less than 110 mm in the calculation of horizontal distance. It works well for pallet positioning and is of high practical value.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009190
Abstract:
Low efficiency, missed diagnosis and misdiagnosis exist in the manual diagnosis and classification of fundus retinal images. To this end, a convolutional network model based on the attention mechanism SENet and GBDT gradient boosting classification method is proposed to help physicians distinguish the fundus screening results of various diseases and reduce the rate of missed and false detection. Based on the deep learning model, the sampling convolutional network is applied to learn the extracted three characteristics of retinal hemorrhage, optic disc edema and macular degeneration, and the GBDT gradient boosting method is employed for identification and classification. The real clinical data provided by the Third People’s Hospital of Dalian are used to evaluate the performance of the proposed method. The results show that the average accuracy, precision, and recall rates of the model reach 99.27%, 98.35%, and0.9810 respectively, and the model has certain practical value in the clinical diagnosis of retinal diseases.
Low efficiency, missed diagnosis and misdiagnosis exist in the manual diagnosis and classification of fundus retinal images. To this end, a convolutional network model based on the attention mechanism SENet and GBDT gradient boosting classification method is proposed to help physicians distinguish the fundus screening results of various diseases and reduce the rate of missed and false detection. Based on the deep learning model, the sampling convolutional network is applied to learn the extracted three characteristics of retinal hemorrhage, optic disc edema and macular degeneration, and the GBDT gradient boosting method is employed for identification and classification. The real clinical data provided by the Third People’s Hospital of Dalian are used to evaluate the performance of the proposed method. The results show that the average accuracy, precision, and recall rates of the model reach 99.27%, 98.35%, and
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009192
Abstract:
The dialogue system that introduces structured knowledge has attracted widespread attention as it can generate more fluent and diverse dialogue replies. However, previous studies only focus on entities in structured knowledge, ignoring the relation between entities and the integrity of knowledge. In this study, a knowledge-aware conversation generation (KCG) model based on the graph convolutional network is proposed. The semantic information of the entity and relation is captured by the knowledge encoder and the representation of the entity is enhanced by the graph convolutional network. Then, the knowledge selection module is applied to obtain the knowledge selection probability distribution of the entities and relations related to the dialogue context. Finally, the knowledge selection probability distribution is fused with the vocabulary probability distribution so that the decoder can select the knowledge or words. In this study, the experiments are conducted on DuConv, a Chinese public data set. The results show that KCG is superior to the current baseline model in terms of automatic evaluation metrics and can generate more fluent and informative replies.
The dialogue system that introduces structured knowledge has attracted widespread attention as it can generate more fluent and diverse dialogue replies. However, previous studies only focus on entities in structured knowledge, ignoring the relation between entities and the integrity of knowledge. In this study, a knowledge-aware conversation generation (KCG) model based on the graph convolutional network is proposed. The semantic information of the entity and relation is captured by the knowledge encoder and the representation of the entity is enhanced by the graph convolutional network. Then, the knowledge selection module is applied to obtain the knowledge selection probability distribution of the entities and relations related to the dialogue context. Finally, the knowledge selection probability distribution is fused with the vocabulary probability distribution so that the decoder can select the knowledge or words. In this study, the experiments are conducted on DuConv, a Chinese public data set. The results show that KCG is superior to the current baseline model in terms of automatic evaluation metrics and can generate more fluent and informative replies.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009197
Abstract:
In this study, a new target detection method based on YOLOv5s is introduced to make up for the deficiencies of the current mainstream detection methods in terms of detection precision and missed detection of small target helmet wearing. Firstly, a small target detection layer is added to increase the detection precision of the small target helmet. Secondly, the ShuffleAttention mechanism is introduced. The number of ShuffleAttention groups is reduced from 64 to 16 in this study, which is more conducive to the global extraction of the depth and size of the model. Finally, the SA-BiFPN network structure is added to carry out the bidirectional multi-scale feature fusion to extract more effective feature information. Experiments show that compared with the original YOLOv5s algorithm, the average precision of the improved algorithm is increased by 1.7%, reaching 92.5%. The average precision of the algorithms with and without helmets is increased by 1.9% and 1.4% respectively. The proposed detection algorithm is compared with other target detection algorithms. The experimental results show that the SAB-YOLOv5s algorithm model is only 1.5M larger than the original YOLOv5s algorithm model, which is smaller than other algorithm models. It improves the average precision of target detection, reduces the probability of missing and false detection in small target detection, and achieves accurate and lightweight helmet wearing detection.
In this study, a new target detection method based on YOLOv5s is introduced to make up for the deficiencies of the current mainstream detection methods in terms of detection precision and missed detection of small target helmet wearing. Firstly, a small target detection layer is added to increase the detection precision of the small target helmet. Secondly, the ShuffleAttention mechanism is introduced. The number of ShuffleAttention groups is reduced from 64 to 16 in this study, which is more conducive to the global extraction of the depth and size of the model. Finally, the SA-BiFPN network structure is added to carry out the bidirectional multi-scale feature fusion to extract more effective feature information. Experiments show that compared with the original YOLOv5s algorithm, the average precision of the improved algorithm is increased by 1.7%, reaching 92.5%. The average precision of the algorithms with and without helmets is increased by 1.9% and 1.4% respectively. The proposed detection algorithm is compared with other target detection algorithms. The experimental results show that the SAB-YOLOv5s algorithm model is only 1.5M larger than the original YOLOv5s algorithm model, which is smaller than other algorithm models. It improves the average precision of target detection, reduces the probability of missing and false detection in small target detection, and achieves accurate and lightweight helmet wearing detection.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009187
Abstract:
To solve the problems of difficult decision-making, multiple interference factors, poor real-time performance and the realization of global optimization in maritime search and rescue (SAR) resource scheduling, this study employs an improved non-dominated sorting genetic (NSGA-II) algorithm by taking the Yellow Sea and the Bohai Sea as an example. Firstly, a multi-objective optimization model for maritime SAR resources is built based on AIS and BeiDou data. Secondly, the normal distribution crossover (NDX)-based operator is adopted by the improved NSGA-II algorithm to avoid falling into local optimum on the basis of expanding the search scope, and a complete Pareto solution set for the multi-objective problem is obtained. The comprehensive evaluation method (TOPSIS) is applied to obtain a compromise solution from the Pareto solution set, namely the optimal design of the search and rescue scheduling scheme. Finally, when the constraint factors such as the number of ships and time are considered, the improved NSGA-II algorithm is employed and compared with the NSGA-II and greedy algorithms. The simulations of the resource scheduling are carried out using the data collected from ships in the Yellow Sea and the Bohai Sea. The results show that the algorithm can effectively solve the problem of maritime SAR resource scheduling optimization.
To solve the problems of difficult decision-making, multiple interference factors, poor real-time performance and the realization of global optimization in maritime search and rescue (SAR) resource scheduling, this study employs an improved non-dominated sorting genetic (NSGA-II) algorithm by taking the Yellow Sea and the Bohai Sea as an example. Firstly, a multi-objective optimization model for maritime SAR resources is built based on AIS and BeiDou data. Secondly, the normal distribution crossover (NDX)-based operator is adopted by the improved NSGA-II algorithm to avoid falling into local optimum on the basis of expanding the search scope, and a complete Pareto solution set for the multi-objective problem is obtained. The comprehensive evaluation method (TOPSIS) is applied to obtain a compromise solution from the Pareto solution set, namely the optimal design of the search and rescue scheduling scheme. Finally, when the constraint factors such as the number of ships and time are considered, the improved NSGA-II algorithm is employed and compared with the NSGA-II and greedy algorithms. The simulations of the resource scheduling are carried out using the data collected from ships in the Yellow Sea and the Bohai Sea. The results show that the algorithm can effectively solve the problem of maritime SAR resource scheduling optimization.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009185
Abstract:
The existing detection method of ceramic tile surface defects has the problem of insufficient ability to identify small target defects, and the detection speed needs to be improved. Therefore, this study proposes a ceramic tile surface defect detection method based on improved YOLOv5. Firstly, due to the small size of ceramic tile surface defects, the detection abilities of three target detection head branches of YOLOv5s are compared and analyzed. It is found that the effectiveness of the model that removes the large target detection head and retains only the medium and small target detection heads is optimal. Secondly, to further realize the lightweight of the model, the study applies ghost convolution and C3Ghost modules to replace the ordinary convolution and C3 modules of YOLOv5s in the Backbone network, thus reducing the number of model parameters and the calculation amount. Finally, the coordinate attention mechanism module is added at the end of the Backbone and Neck networks of YOLOv5s to solve the problem of no attention preference in the original model. The proposed method is tested on the Tianchi ceramic tile defect detection dataset. The results show that the mean precision of the improved detection model averages 66%, which is 1.8% higher than the original YOLOv5s model. Besides, the size of the model is only 10.14 MB, and the number of parameters and the calculation amount is reduced by 48.7% and 38.7% respectively compared with the original model.
The existing detection method of ceramic tile surface defects has the problem of insufficient ability to identify small target defects, and the detection speed needs to be improved. Therefore, this study proposes a ceramic tile surface defect detection method based on improved YOLOv5. Firstly, due to the small size of ceramic tile surface defects, the detection abilities of three target detection head branches of YOLOv5s are compared and analyzed. It is found that the effectiveness of the model that removes the large target detection head and retains only the medium and small target detection heads is optimal. Secondly, to further realize the lightweight of the model, the study applies ghost convolution and C3Ghost modules to replace the ordinary convolution and C3 modules of YOLOv5s in the Backbone network, thus reducing the number of model parameters and the calculation amount. Finally, the coordinate attention mechanism module is added at the end of the Backbone and Neck networks of YOLOv5s to solve the problem of no attention preference in the original model. The proposed method is tested on the Tianchi ceramic tile defect detection dataset. The results show that the mean precision of the improved detection model averages 66%, which is 1.8% higher than the original YOLOv5s model. Besides, the size of the model is only 10.14 MB, and the number of parameters and the calculation amount is reduced by 48.7% and 38.7% respectively compared with the original model.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009164
Abstract:
As an important load-bearing element of cable-stayed bridges, vibration testing of stay cables plays a key role in bridge health monitoring. Under ideal laboratory conditions, the traditional vibration detection algorithm with spatial phase can achieve high-accuracy measurement of structural vibration. However, in practical scenarios, environmental factors such as vehicles, wind excitation, and the angle between the cable and the ground can cause large errors in the measurement results. Therefore, the traditional algorithm is not suitable for cable vibration detection in these cases. To address this problem, this study proposes a cable vibration frequency detection algorithm based on directional adaptive complex steerable filters to precisely measure cable vibration in real scenarios. Firstly, the linear characteristics of the cable are used to detect the location of the cable and determine the main vibration direction of the cable; secondly, according to the vibration direction characteristics of the cable, a directional adaptive complex steerable filter is designed to decompose each frame of the video, so as to obtain the phase and amplitude spectra of the same direction at different scales and enhance the phase of the edge region of the cable. Finally, the spatial phase of each frame is averaged, and the phase sequences are arranged in time order to obtain the main frequency of cable vibration by Fourier transform. By comparing the results with those of acceleration sensors, it is proved that the proposed algorithm is highly robust and can meet the application requirements of bridge cable vibration measurement in real scenarios.
As an important load-bearing element of cable-stayed bridges, vibration testing of stay cables plays a key role in bridge health monitoring. Under ideal laboratory conditions, the traditional vibration detection algorithm with spatial phase can achieve high-accuracy measurement of structural vibration. However, in practical scenarios, environmental factors such as vehicles, wind excitation, and the angle between the cable and the ground can cause large errors in the measurement results. Therefore, the traditional algorithm is not suitable for cable vibration detection in these cases. To address this problem, this study proposes a cable vibration frequency detection algorithm based on directional adaptive complex steerable filters to precisely measure cable vibration in real scenarios. Firstly, the linear characteristics of the cable are used to detect the location of the cable and determine the main vibration direction of the cable; secondly, according to the vibration direction characteristics of the cable, a directional adaptive complex steerable filter is designed to decompose each frame of the video, so as to obtain the phase and amplitude spectra of the same direction at different scales and enhance the phase of the edge region of the cable. Finally, the spatial phase of each frame is averaged, and the phase sequences are arranged in time order to obtain the main frequency of cable vibration by Fourier transform. By comparing the results with those of acceleration sensors, it is proved that the proposed algorithm is highly robust and can meet the application requirements of bridge cable vibration measurement in real scenarios.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009160
Abstract:
When the basic Q-learning algorithm is applied to path planning, the randomness of action selection makes the early search efficiency of the algorithm low and the planning time-consuming, and even a complete and feasible path cannot be found. Therefore, a path planning algorithm of robots based on improved ant colony optimization (ACO) and dynamic Q-learning fusion is proposed. The pheromone increment mechanism of the elite ant model and sorting ant model is used, and a new pheromone increment updating method is designed to improve the exploration efficiency of robots. The pheromone matrix of the improved ant colony optimization algorithm is used to assign values to the Q table, so as to reduce the ineffective exploration of the robot at the initial stage. In addition, a dynamic selection strategy is designed to improve the convergence speed and the stability of the algorithm. Finally, different simulation experiments are carried out on two-dimensional static grid maps with different obstacle levels. The results show that the proposed method can effectively reduce the number of iterations and optimization time consumption in the optimization process.
When the basic Q-learning algorithm is applied to path planning, the randomness of action selection makes the early search efficiency of the algorithm low and the planning time-consuming, and even a complete and feasible path cannot be found. Therefore, a path planning algorithm of robots based on improved ant colony optimization (ACO) and dynamic Q-learning fusion is proposed. The pheromone increment mechanism of the elite ant model and sorting ant model is used, and a new pheromone increment updating method is designed to improve the exploration efficiency of robots. The pheromone matrix of the improved ant colony optimization algorithm is used to assign values to the Q table, so as to reduce the ineffective exploration of the robot at the initial stage. In addition, a dynamic selection strategy is designed to improve the convergence speed and the stability of the algorithm. Finally, different simulation experiments are carried out on two-dimensional static grid maps with different obstacle levels. The results show that the proposed method can effectively reduce the number of iterations and optimization time consumption in the optimization process.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009167
Abstract:
The ground images obtained by the unmanned aerial vehicle (UAV) platform have a high spatial resolution, but they also bring a lot of “interference” to crop classification while providing rich details. In particular, when depth models are used for crop recognition, there are problems such as insufficient edge information extraction and misclassification of similarly textured crops, which results in a poor classification effect. Therefore, a model is constructed by the idea of multi-scale attention feature extraction to effectively extract edge information and improve the accuracy of crop classification. The proposed multi-scale attention network (MSAT) obtains crop information on different scales at the same level through multi-scale block embedding. The multi-scale feature map is mapped into multiple sequences that are fed into the factor attention module independently, which enhances the attention to crop contexts and improves the model’s extraction ability of plot edge information. Moreover, the built-in convolutional relative position encoding of the factor attention module enhances the modeling of local information inside the module and the ability to distinguish similarly textured crops. Finally, the thickness information is extracted upon the fusion of local features and global features. The classification results of rice, sugarcane, corn, bananas, and oranges show that the mean intersection over union (MIoU) and overall accuracy (OA) of the MSAT model reach 0.816 and 98.10%, respectively, which verifies that the fine crop classification method based on high-resolution images is feasible, and the equipment cost is low.
The ground images obtained by the unmanned aerial vehicle (UAV) platform have a high spatial resolution, but they also bring a lot of “interference” to crop classification while providing rich details. In particular, when depth models are used for crop recognition, there are problems such as insufficient edge information extraction and misclassification of similarly textured crops, which results in a poor classification effect. Therefore, a model is constructed by the idea of multi-scale attention feature extraction to effectively extract edge information and improve the accuracy of crop classification. The proposed multi-scale attention network (MSAT) obtains crop information on different scales at the same level through multi-scale block embedding. The multi-scale feature map is mapped into multiple sequences that are fed into the factor attention module independently, which enhances the attention to crop contexts and improves the model’s extraction ability of plot edge information. Moreover, the built-in convolutional relative position encoding of the factor attention module enhances the modeling of local information inside the module and the ability to distinguish similarly textured crops. Finally, the thickness information is extracted upon the fusion of local features and global features. The classification results of rice, sugarcane, corn, bananas, and oranges show that the mean intersection over union (MIoU) and overall accuracy (OA) of the MSAT model reach 0.816 and 98.10%, respectively, which verifies that the fine crop classification method based on high-resolution images is feasible, and the equipment cost is low.
Available online: May 22, 2023 , DOI: 10.15888/j.cnki.csa.009168
Abstract:
Considering the problems caused by insufficient attention to receptive field scale and inadequate extraction of feature channel information in existing super-resolution reconstruction models for optical remote sensing images, this study proposes a new super-resolution reconstruction model for optical remote sensing images, which is based on multi-scale feature extraction and coordinate attention. On the basis of the deep residual network structure, some cascaded multi-scale feature & coordinate attention blocks (MFCABs) are designed in the high-frequency branch of the network to fully explore the high-frequency features of the input low-resolution images. Firstly, the Inception submodule is introduced into MFCABs to capture spatial features under different receptive fields by convolution kernels of different scales. Secondly, the coordinate attention submodule is added after the Inception submodule, and attention is paid to the channel and coordinate dimensions to obtain a better channel attention effect. Finally, the features extracted by each MFCAB are fused in multiple paths to realize the effective fusion of multi-scale spatial information and multi-channel attention information. In the double and triple magnification of the MFCAB model on the NWPU4500 dataset, the PSNR reaches 34.73 dB and 30.12 dB, respectively, which is 0.66 dB and 0.01 dB higher than EDSR. In the double, triple, and quadruple magnification of the model on the AID1600 dataset, the PSNR reaches 34.71 dB, 30.58 dB, and 28.44 dB, respectively, which is 0.09 dB, 0.03 dB, and 0.04 dB higher than EDSR. The experimental results show that the reconstruction effect of this model on the optical remote sensing image datasets is better than the mainstream super-resolution image reconstruction model.
Considering the problems caused by insufficient attention to receptive field scale and inadequate extraction of feature channel information in existing super-resolution reconstruction models for optical remote sensing images, this study proposes a new super-resolution reconstruction model for optical remote sensing images, which is based on multi-scale feature extraction and coordinate attention. On the basis of the deep residual network structure, some cascaded multi-scale feature & coordinate attention blocks (MFCABs) are designed in the high-frequency branch of the network to fully explore the high-frequency features of the input low-resolution images. Firstly, the Inception submodule is introduced into MFCABs to capture spatial features under different receptive fields by convolution kernels of different scales. Secondly, the coordinate attention submodule is added after the Inception submodule, and attention is paid to the channel and coordinate dimensions to obtain a better channel attention effect. Finally, the features extracted by each MFCAB are fused in multiple paths to realize the effective fusion of multi-scale spatial information and multi-channel attention information. In the double and triple magnification of the MFCAB model on the NWPU4500 dataset, the PSNR reaches 34.73 dB and 30.12 dB, respectively, which is 0.66 dB and 0.01 dB higher than EDSR. In the double, triple, and quadruple magnification of the model on the AID1600 dataset, the PSNR reaches 34.71 dB, 30.58 dB, and 28.44 dB, respectively, which is 0.09 dB, 0.03 dB, and 0.04 dB higher than EDSR. The experimental results show that the reconstruction effect of this model on the optical remote sensing image datasets is better than the mainstream super-resolution image reconstruction model.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009175
Abstract:
For a variety of crop disease and pest images, it is difficult to achieve satisfactory accuracy due to the technical problems of various diseases and pests and similar characteristics of small targets in the natural environment. In this study, a pest detection and identification model, namely YOLOv5-EB that enhances the fusion of local feature and global feature information in the natural background is proposed, and experiments are carried out on the published large-scale pest dataset IP102. The results show that the accuracy of this study is improved by five percentage points compared with the YOLOv5 model. The MLP operation of replacing channel attention in CBAM with one-dimensional convolution is introduced, which optimizes the problem that channel attention is easy to ignore the information interaction in the channel after global processing. Secondly, the Focus operation is replaced by 6×6 convolution to enhance the ability to extract pest features. The experimental results show that the average accuracy of YOLOv5-EB reaches 87% in detecting pests, which not only effectively improves the identification performance of crop pest images but also increases the detection speed compared with Faster R-CNN, EfficientDet, YOLOv3, YOLOv4, and YOLOv5 models. The study reveals that the YOLOv5-EB algorithm meets the accuracy and real-time requirements of target detection of various crop diseases and pests.
For a variety of crop disease and pest images, it is difficult to achieve satisfactory accuracy due to the technical problems of various diseases and pests and similar characteristics of small targets in the natural environment. In this study, a pest detection and identification model, namely YOLOv5-EB that enhances the fusion of local feature and global feature information in the natural background is proposed, and experiments are carried out on the published large-scale pest dataset IP102. The results show that the accuracy of this study is improved by five percentage points compared with the YOLOv5 model. The MLP operation of replacing channel attention in CBAM with one-dimensional convolution is introduced, which optimizes the problem that channel attention is easy to ignore the information interaction in the channel after global processing. Secondly, the Focus operation is replaced by 6×6 convolution to enhance the ability to extract pest features. The experimental results show that the average accuracy of YOLOv5-EB reaches 87% in detecting pests, which not only effectively improves the identification performance of crop pest images but also increases the detection speed compared with Faster R-CNN, EfficientDet, YOLOv3, YOLOv4, and YOLOv5 models. The study reveals that the YOLOv5-EB algorithm meets the accuracy and real-time requirements of target detection of various crop diseases and pests.
ZHANG Shuo, YANG Shi-Yin, SUN Bo-Yu, XU Yun-Fei, SHEN Hao, LYU Yong-Jia, JIA Yun-Peng, XING Hui-Ming, ZHAO Xin-Hua
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009089
Abstract:
Intelligent protection for rail vehicles involves the tasks of railway track intrusion detection and driving area segmentation. In the field of deep learning, there are algorithms for each task, but they cannot meet the needs of multi-task situations very well. This algorithm uses a lightweight convolution neural network (CNN) as an encoder to extract the feature map and then sends it to two decoders based on one-stage detection network to complete their respective tasks. Semantic features of different levels and scales are fused in the feature map output by the encoder, which performs pixel-level semantic prediction well and improves the detection and segmentation performance significantly. The equipment using this algorithm will master the recognition, detection, judgment, and tracking of new targets, ensuring the traveling safety of rail vehicles.
Intelligent protection for rail vehicles involves the tasks of railway track intrusion detection and driving area segmentation. In the field of deep learning, there are algorithms for each task, but they cannot meet the needs of multi-task situations very well. This algorithm uses a lightweight convolution neural network (CNN) as an encoder to extract the feature map and then sends it to two decoders based on one-stage detection network to complete their respective tasks. Semantic features of different levels and scales are fused in the feature map output by the encoder, which performs pixel-level semantic prediction well and improves the detection and segmentation performance significantly. The equipment using this algorithm will master the recognition, detection, judgment, and tracking of new targets, ensuring the traveling safety of rail vehicles.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009123
Abstract:
In order to reduce the security risk of network slices caused by infrastructure sharing in network function virtualization (NFV) architecture and take into account the availability of network slices during deployment, this study proposes a security deployment method of network slices based on Brewer-Nash (BN) model. The deployment method first proposes a fifth-generation (5G) slice deployment architecture based on BN model. Based on BN model, the security deployment rules of virtual network function (VNF) in network slices are designed to make VNF of vertical user network slices with different conflict of interest classes form host isolation, which improves the runtime security of VNF. Then the deployment problem is modeled by integer linear programming, and the objective function is to minimize the deployment cost. Finally, the deployment result is obtained by a genetic algorithm. Simulation results show that the proposed method can reduce the deployment cost under the premise of meeting the security deployment rules of VNF of network slices.
In order to reduce the security risk of network slices caused by infrastructure sharing in network function virtualization (NFV) architecture and take into account the availability of network slices during deployment, this study proposes a security deployment method of network slices based on Brewer-Nash (BN) model. The deployment method first proposes a fifth-generation (5G) slice deployment architecture based on BN model. Based on BN model, the security deployment rules of virtual network function (VNF) in network slices are designed to make VNF of vertical user network slices with different conflict of interest classes form host isolation, which improves the runtime security of VNF. Then the deployment problem is modeled by integer linear programming, and the objective function is to minimize the deployment cost. Finally, the deployment result is obtained by a genetic algorithm. Simulation results show that the proposed method can reduce the deployment cost under the premise of meeting the security deployment rules of VNF of network slices.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009130
Abstract:
In order to effectively solve the problem of target tracking drift or loss in the face of large-scale deformation, complete occlusion, background interference, and other complex scenes, a multi-branch Siamese network target tracking algorithm (SiamMB) is proposed. First, the method of enhancing the network robustness of adjacent frame branches is used to improve the discrimination ability of target features in the search frame and strengthen the robustness of the model. Secondly, the spatial attention network is fused, and different weights are applied to the features of different spatial positions. In addition, the features that are beneficial to target tracking in spatial positions are emphasized, so as to improve the discriminability of the model. Finally, evaluation is performed on OTB2015 and VOT2018 datasets, and the results show that the tracking accuracy and success rate of SiamMB reach 91.8% and 71.8%, respectively, which makes SiamMB more competitive than the current mainstream tracking algorithms.
In order to effectively solve the problem of target tracking drift or loss in the face of large-scale deformation, complete occlusion, background interference, and other complex scenes, a multi-branch Siamese network target tracking algorithm (SiamMB) is proposed. First, the method of enhancing the network robustness of adjacent frame branches is used to improve the discrimination ability of target features in the search frame and strengthen the robustness of the model. Secondly, the spatial attention network is fused, and different weights are applied to the features of different spatial positions. In addition, the features that are beneficial to target tracking in spatial positions are emphasized, so as to improve the discriminability of the model. Finally, evaluation is performed on OTB2015 and VOT2018 datasets, and the results show that the tracking accuracy and success rate of SiamMB reach 91.8% and 71.8%, respectively, which makes SiamMB more competitive than the current mainstream tracking algorithms.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009097
Abstract:
Event extraction is a key research area in information extraction. To improve the effect of event extraction and solve the problem that general event extraction methods cannot make full use of text feature information, an event extraction method fused with trigger word features is proposed. A remote trigger word database is constructed to provide additional feature information for the event classification model and enhance the discovery ability of event trigger words. Then, the event type and the distance features of trigger words are integrated to improve the representation and learning ability of the event element extraction model. Finally, the event classification model and the event element extraction model are connected in series to improve the event extraction effect. Experiments on the DuEE dataset demonstrate that compared with other models, this model improves the accuracy, recall, and F1 value, which proves the effectiveness of this model.
Event extraction is a key research area in information extraction. To improve the effect of event extraction and solve the problem that general event extraction methods cannot make full use of text feature information, an event extraction method fused with trigger word features is proposed. A remote trigger word database is constructed to provide additional feature information for the event classification model and enhance the discovery ability of event trigger words. Then, the event type and the distance features of trigger words are integrated to improve the representation and learning ability of the event element extraction model. Finally, the event classification model and the event element extraction model are connected in series to improve the event extraction effect. Experiments on the DuEE dataset demonstrate that compared with other models, this model improves the accuracy, recall, and F1 value, which proves the effectiveness of this model.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009057
Abstract:
In recent years, due to the rapid development of artificial intelligence in the medical field, the demand for medical images from researchers has been increasing day by day. These medical images often need to be finely annotated before being put into use. Compared with natural images, the data annotation of medical images is more specialized and complex. Therefore, medical images face the problems of low annotation rate and high annotation cost, resulting in the scarcity of labeled samples. Fundus images, as an important medical image, can achieve the screening and primary diagnosis of most ophthalmic diseases such as diabetic retinopathy and glaucoma, but they also face some difficulty in annotation. To address this situation, this study designs and develops an efficient semi-automated annotation system for fundus images, which is innovative in that it can perform semi-automated annotation of multiple eye diseases. Various diseases are predicted based on the fundus images, and the types of prediction results include disease classification and lesion segmentation. The annotator only needs to review and modify the generated prediction results, and this process can greatly reduce the workload of the annotator. In addition, the system includes four modules: user management, project management, image management, and algorithm model management. These four modules enable task assignment in team annotation, visualization of annotation progress data, quick export of annotation results, and other user-friendly functions. The system greatly improves the annotation efficiency and experience of annotators.
In recent years, due to the rapid development of artificial intelligence in the medical field, the demand for medical images from researchers has been increasing day by day. These medical images often need to be finely annotated before being put into use. Compared with natural images, the data annotation of medical images is more specialized and complex. Therefore, medical images face the problems of low annotation rate and high annotation cost, resulting in the scarcity of labeled samples. Fundus images, as an important medical image, can achieve the screening and primary diagnosis of most ophthalmic diseases such as diabetic retinopathy and glaucoma, but they also face some difficulty in annotation. To address this situation, this study designs and develops an efficient semi-automated annotation system for fundus images, which is innovative in that it can perform semi-automated annotation of multiple eye diseases. Various diseases are predicted based on the fundus images, and the types of prediction results include disease classification and lesion segmentation. The annotator only needs to review and modify the generated prediction results, and this process can greatly reduce the workload of the annotator. In addition, the system includes four modules: user management, project management, image management, and algorithm model management. These four modules enable task assignment in team annotation, visualization of annotation progress data, quick export of annotation results, and other user-friendly functions. The system greatly improves the annotation efficiency and experience of annotators.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009176
Abstract:
Integrated avionics system is an important feature of the new generation of aircraft, and its reliability and stability play a decisive role in the flight and safety of the entire aircraft. As the avionics system should possess high reliability, a distributed cluster redundancy architecture is proposed, and the corresponding redundancy management scheme is designed to tolerate Byzantine errors that may occur after avionics system failure and effectively improve the reliability and fault tolerance of fault-tolerant computers. The proposed redundancy management scheme is optimized by the two schemes of threshold signature and cluster selection to reduce the communication overhead between redundancy computers in the cluster, avoid affecting the real-time performance of the avionics system, and improve the redundancy management efficiency. Through simulation experiments, the results verify that the distributed cluster redundancy management scheme can effectively improve the reliability of the avionics system and enhance Byzantine resilience. Meanwhile, in an n-redundancy avionics system, the system can still operate correctly as long as the number of Byzantine nodes is less than n/3, and the optimization scheme has lower communication and computing costs.
Integrated avionics system is an important feature of the new generation of aircraft, and its reliability and stability play a decisive role in the flight and safety of the entire aircraft. As the avionics system should possess high reliability, a distributed cluster redundancy architecture is proposed, and the corresponding redundancy management scheme is designed to tolerate Byzantine errors that may occur after avionics system failure and effectively improve the reliability and fault tolerance of fault-tolerant computers. The proposed redundancy management scheme is optimized by the two schemes of threshold signature and cluster selection to reduce the communication overhead between redundancy computers in the cluster, avoid affecting the real-time performance of the avionics system, and improve the redundancy management efficiency. Through simulation experiments, the results verify that the distributed cluster redundancy management scheme can effectively improve the reliability of the avionics system and enhance Byzantine resilience. Meanwhile, in an n-redundancy avionics system, the system can still operate correctly as long as the number of Byzantine nodes is less than n/3, and the optimization scheme has lower communication and computing costs.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009169
Abstract:
The processing of device tasks in the industrial Internet requires a large amount of computing resources, and the tasks with low latency requirements have increased significantly. Edge computing places computing power and other resources on the side close to the demand to provide effective support for task processing. However, due to the limited edge computing resources, the requirements of low latency and high completion rate of the device tasks cannot be satisfied at the same time. It is still a great challenge to determine a reasonable offloading decision and task scheduling. Given the above problem, a deep learning-based dynamic priority task scheduling algorithm DPTSA is proposed in this study. Firstly, the tasks to be processed are selected according to dynamic priority and task scheduling decisions are generated through neural networks. Then, a set of feasible solutions are generated through cross-variance and other operations, and the optimal solutions are screened out and stored in the empirical buffer area. Finally, the neural network parameters are optimized through the empirical buffer samples. The experimental results based on Google’s Brog task scheduling dataset show that DPTSA is superior to the four benchmark algorithms in terms of task waiting time and task completion rate.
The processing of device tasks in the industrial Internet requires a large amount of computing resources, and the tasks with low latency requirements have increased significantly. Edge computing places computing power and other resources on the side close to the demand to provide effective support for task processing. However, due to the limited edge computing resources, the requirements of low latency and high completion rate of the device tasks cannot be satisfied at the same time. It is still a great challenge to determine a reasonable offloading decision and task scheduling. Given the above problem, a deep learning-based dynamic priority task scheduling algorithm DPTSA is proposed in this study. Firstly, the tasks to be processed are selected according to dynamic priority and task scheduling decisions are generated through neural networks. Then, a set of feasible solutions are generated through cross-variance and other operations, and the optimal solutions are screened out and stored in the empirical buffer area. Finally, the neural network parameters are optimized through the empirical buffer samples. The experimental results based on Google’s Brog task scheduling dataset show that DPTSA is superior to the four benchmark algorithms in terms of task waiting time and task completion rate.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009163
Abstract:
To improve the identification accuracy of ordinary neural convolutional networks for tomato leaf disease, a new network based on the multi-scale fusion attention mechanism (MIPSANet) is proposed. The lightweight network is used as the main framework to reduce the network parameters in this network. To increase the depth and width of the network, the Inception structure is added to extract multi-scale feature information of data. Meanwhile, a more elaborate dual attention mechanism, polarized self-attention (PSA), is used in this process as a plug-and-play module to be embedded in the whole model, which improves the expressive power of important feature points. The lightweight PSA modules are also suitable for this model. A full connection layer is added after the convolution for classification. The proposed MIPSANet is applied to conduct experiments on Kaggle public dataset, tomato leaves dataset, with 30 batches of training, achieving an accuracy rate of 91.05%. The results show that this network is strikingly effective in the classification of tomato leaf diseases compared with other networks, which provides some reference value for the network structure and parameter configuration of the classification network.
To improve the identification accuracy of ordinary neural convolutional networks for tomato leaf disease, a new network based on the multi-scale fusion attention mechanism (MIPSANet) is proposed. The lightweight network is used as the main framework to reduce the network parameters in this network. To increase the depth and width of the network, the Inception structure is added to extract multi-scale feature information of data. Meanwhile, a more elaborate dual attention mechanism, polarized self-attention (PSA), is used in this process as a plug-and-play module to be embedded in the whole model, which improves the expressive power of important feature points. The lightweight PSA modules are also suitable for this model. A full connection layer is added after the convolution for classification. The proposed MIPSANet is applied to conduct experiments on Kaggle public dataset, tomato leaves dataset, with 30 batches of training, achieving an accuracy rate of 91.05%. The results show that this network is strikingly effective in the classification of tomato leaf diseases compared with other networks, which provides some reference value for the network structure and parameter configuration of the classification network.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009196
Abstract:
Diversion in severe weather is closely related to the designation of forbidden areas and path planning algorithms. Given the large invalid area in the Graham scanning results in the construction of the diversion environment, this study proposes a delineation method of Graham parallel scanning after the area is divided into blocks. For the sudden occurrence of severe weather and complex environments, the study proposes a dynamic programming method of composite structure conducting intelligent segmentation and ant colony algorithm local search based on incremental D*Lite global planning path. The pheromone updating strategy is improved to solve the shortcomings of slow convergence speed, long time consumed, and tendency to fall into local optimum. The experimental results show that the shape of the flight forbidden areas designated by Graham parallel scanning based on the divided blocks is closer to reality, and the area is reduced to 48.1% of the original one. D*Lite-ACO, an improved ant colony fusion D*Lite dynamic path planning algorithm for composite structures, takes both the global and local area into account and controls the replanning range between the current position and the targeted point. The evaluation metrics in path length, planning time, and iteration range are improved by 1.2%, 40.7%, and 66.7%, respectively.
Diversion in severe weather is closely related to the designation of forbidden areas and path planning algorithms. Given the large invalid area in the Graham scanning results in the construction of the diversion environment, this study proposes a delineation method of Graham parallel scanning after the area is divided into blocks. For the sudden occurrence of severe weather and complex environments, the study proposes a dynamic programming method of composite structure conducting intelligent segmentation and ant colony algorithm local search based on incremental D*Lite global planning path. The pheromone updating strategy is improved to solve the shortcomings of slow convergence speed, long time consumed, and tendency to fall into local optimum. The experimental results show that the shape of the flight forbidden areas designated by Graham parallel scanning based on the divided blocks is closer to reality, and the area is reduced to 48.1% of the original one. D*Lite-ACO, an improved ant colony fusion D*Lite dynamic path planning algorithm for composite structures, takes both the global and local area into account and controls the replanning range between the current position and the targeted point. The evaluation metrics in path length, planning time, and iteration range are improved by 1.2%, 40.7%, and 66.7%, respectively.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009202
Abstract:
Non-intrusive load decomposition is an important part of the intelligent power consumption system, which can deeply analyze the power consumption information of users and is of great significance to load forecasting, demand side management, and power grid security. This study proposes a non-intrusive load decomposition method based on the improved particle swarm optimization factorial hidden Markov model (IPSO-FHMM). Gaussian mixture model (GMM) is used to cluster the states of individual loads. The total load model is represented by an FHMM. Since the Baum-Welch algorithm tends to converge to the local extremum, the PSO algorithm with linearly decreasing weights is introduced into the parameter training of FHMM. Simulation experiments using the AMPds2 dataset show that the model can effectively improve the decomposition accuracy.
Non-intrusive load decomposition is an important part of the intelligent power consumption system, which can deeply analyze the power consumption information of users and is of great significance to load forecasting, demand side management, and power grid security. This study proposes a non-intrusive load decomposition method based on the improved particle swarm optimization factorial hidden Markov model (IPSO-FHMM). Gaussian mixture model (GMM) is used to cluster the states of individual loads. The total load model is represented by an FHMM. Since the Baum-Welch algorithm tends to converge to the local extremum, the PSO algorithm with linearly decreasing weights is introduced into the parameter training of FHMM. Simulation experiments using the AMPds2 dataset show that the model can effectively improve the decomposition accuracy.
Available online: May 19, 2023 , DOI: 10.15888/j.cnki.csa.009203
Abstract:
Currently, most augmented reality and autonomous driving applications use not only the depth information estimated by the depth network but also the pose information estimated by the pose network. Integrating both the pose network and the depth network into an embedded device can be extremely memory-consuming. In view of this problem, a method of the depth and pose networks sharing feature extractors is proposed to keep the model at a lightweight size. In addition, the depth-separable convolutional lightweight depth network with linear structure allows the network to obtain fewer parameters without losing too much detailed information. Finally, experiments on the KITTI dataset show that compared with the algorithms of the same type, the size of the pose and deep network parameters is only 35.33 MB. At the same time, the average absolute error of the restored depth map is also maintained at 0.129.
Currently, most augmented reality and autonomous driving applications use not only the depth information estimated by the depth network but also the pose information estimated by the pose network. Integrating both the pose network and the depth network into an embedded device can be extremely memory-consuming. In view of this problem, a method of the depth and pose networks sharing feature extractors is proposed to keep the model at a lightweight size. In addition, the depth-separable convolutional lightweight depth network with linear structure allows the network to obtain fewer parameters without losing too much detailed information. Finally, experiments on the KITTI dataset show that compared with the algorithms of the same type, the size of the pose and deep network parameters is only 35.33 MB. At the same time, the average absolute error of the restored depth map is also maintained at 0.129.
Available online: May 12, 2023 , DOI: 10.15888/j.cnki.csa.009177
Abstract:
Adding specific perturbations to images can help generate adversarial samples that mislead deep neural networks to output incorrect results. More powerful attack methods can facilitate research on the security and robustness of network models. The attack methods are divided into white-box and black-box attacks, and the transferability of adversarial samples can be used to attack other black-box ones by the results generated by known models. Attacks based on linear integrated gradients (TAIG-S) can generate highly transferable adversarial samples, but they are affected by noise in the linear path, superimposing pixel gradients that are irrelevant to the prediction results, which limits the success rate of attacks. With guided integrated gradients, the proposed Guided-TAIG method uses adaptive adjustment to correct some pixel values with low absolute values on each segment of the integrated path calculation and finds the starting point of the next step within a certain interval, circumventing the accumulation of meaningless gradient noise. The experiments on the ImageNet dataset show that Guided-TAIG outperforms FGSM, C&W, and TAIG-S for white-box attacks on both CNN and Transformer architecture models, produces smaller perturbations, and has better performance for transferable attacks in the black-box mode. This demonstrates the effectiveness of the proposed method.
Adding specific perturbations to images can help generate adversarial samples that mislead deep neural networks to output incorrect results. More powerful attack methods can facilitate research on the security and robustness of network models. The attack methods are divided into white-box and black-box attacks, and the transferability of adversarial samples can be used to attack other black-box ones by the results generated by known models. Attacks based on linear integrated gradients (TAIG-S) can generate highly transferable adversarial samples, but they are affected by noise in the linear path, superimposing pixel gradients that are irrelevant to the prediction results, which limits the success rate of attacks. With guided integrated gradients, the proposed Guided-TAIG method uses adaptive adjustment to correct some pixel values with low absolute values on each segment of the integrated path calculation and finds the starting point of the next step within a certain interval, circumventing the accumulation of meaningless gradient noise. The experiments on the ImageNet dataset show that Guided-TAIG outperforms FGSM, C&W, and TAIG-S for white-box attacks on both CNN and Transformer architecture models, produces smaller perturbations, and has better performance for transferable attacks in the black-box mode. This demonstrates the effectiveness of the proposed method.
Available online: May 12, 2023 , DOI: 10.15888/j.cnki.csa.009172
Abstract:
The image masking method based on semantic segmentation is often used to solve the interference problem of moving objects in three-dimensional (3D) reconstruction tasks of static scenes. However, a small number of invalid feature points will be produced when the mask is used to eliminate moving objects. To solve this problem, a method for eliminating moving objects in the dimension of feature points is proposed. The convolutional neural network is used to obtain the moving target information, and the feature point filtering module is constructed. Then, the moving target information is used to filter and update the feature point list for the complete elimination of the moving target. The ground image dataset and aerial image dataset and the processing algorithms of DeepLabV3 and YOLOv4 are used to verify the proposed method. The results show that the moving object elimination method in 3D reconstruction in the feature point dimension can completely eliminate the moving object without generating additional invalid feature points. Compared with the image masking method, the proposed method shortens the point cloud generation time by 13.36% and reduces the reprojection error by 9.93% on average.
The image masking method based on semantic segmentation is often used to solve the interference problem of moving objects in three-dimensional (3D) reconstruction tasks of static scenes. However, a small number of invalid feature points will be produced when the mask is used to eliminate moving objects. To solve this problem, a method for eliminating moving objects in the dimension of feature points is proposed. The convolutional neural network is used to obtain the moving target information, and the feature point filtering module is constructed. Then, the moving target information is used to filter and update the feature point list for the complete elimination of the moving target. The ground image dataset and aerial image dataset and the processing algorithms of DeepLabV3 and YOLOv4 are used to verify the proposed method. The results show that the moving object elimination method in 3D reconstruction in the feature point dimension can completely eliminate the moving object without generating additional invalid feature points. Compared with the image masking method, the proposed method shortens the point cloud generation time by 13.36% and reduces the reprojection error by 9.93% on average.
Available online: May 12, 2023 , DOI: 10.15888/j.cnki.csa.009173
Abstract:
In the robot visual navigation task of the indoor environment, the detection of the drivable area is an indispensable part, which is the basis for ensuring the realization of the autonomous driving task. At present, many solutions are to detect the drivable area by identifying obstacles in the dataset, which lacks flexibility. Therefore, a drivable area detection method for indoor flat ground such as subway stations is proposed in this study to improve practicability. The classic MobileNetV3 network is applied to classify the collected front images and determine whether they are ground areas. Due to the influences of stickers such as landmarks and arrows on the indoor floor, it is necessary to further judge the non-ground area and distinguish it from conventional three-dimensional obstacles. In this study, the feature point matching between successive frames is adopted to obtain the camera moving distance, and the method of calculating the slope by straight line fitting is used to distinguish between three-dimensional obstacles and plane landmarks. Experiments show that the proposed method can better detect the drivable area in front of the robot and has high practical value.
In the robot visual navigation task of the indoor environment, the detection of the drivable area is an indispensable part, which is the basis for ensuring the realization of the autonomous driving task. At present, many solutions are to detect the drivable area by identifying obstacles in the dataset, which lacks flexibility. Therefore, a drivable area detection method for indoor flat ground such as subway stations is proposed in this study to improve practicability. The classic MobileNetV3 network is applied to classify the collected front images and determine whether they are ground areas. Due to the influences of stickers such as landmarks and arrows on the indoor floor, it is necessary to further judge the non-ground area and distinguish it from conventional three-dimensional obstacles. In this study, the feature point matching between successive frames is adopted to obtain the camera moving distance, and the method of calculating the slope by straight line fitting is used to distinguish between three-dimensional obstacles and plane landmarks. Experiments show that the proposed method can better detect the drivable area in front of the robot and has high practical value.
Available online: May 12, 2023 , DOI: 10.15888/j.cnki.csa.009132
Abstract:
Since it is difficult to assign weights to the importance of safety risk factor indicators in the process of safety risk assessment of informatization systems, this study proposes a safety risk assessment model based on improved D-S evidence theory and fusion weight set with a construction site as the application scenario. Firstly, the safety risk assessment process and elements of the construction site are fully studied, and a safety evaluation system for the construction site is established. Secondly, the D-S synthesis algorithm based on weight assignment and matrix analysis is used to improve the analytic hierarchy process (AHP) method, and the entropy weight method based on data is adopted to calculate the subjective and objective weights of each indicator in the index layer of the evaluation system. Thirdly, the improved D-S evidence fusion algorithm is used to synthesize multi-source evidence to obtain the indicator weights, so as to avoid the one-sidedness of a single assignment and get the optimal comprehensive weight. Finally, the comprehensive evaluation index of the construction site is calculated according to the TOPSIS evaluation algorithm. The analysis shows that the safety risk assessment model based on the improved D-S evidence theory and fusion weight set can effectively assess the safety of construction sites, reduce the uncertainty of assessment results, and improve the credibility of risk assessment results.
Since it is difficult to assign weights to the importance of safety risk factor indicators in the process of safety risk assessment of informatization systems, this study proposes a safety risk assessment model based on improved D-S evidence theory and fusion weight set with a construction site as the application scenario. Firstly, the safety risk assessment process and elements of the construction site are fully studied, and a safety evaluation system for the construction site is established. Secondly, the D-S synthesis algorithm based on weight assignment and matrix analysis is used to improve the analytic hierarchy process (AHP) method, and the entropy weight method based on data is adopted to calculate the subjective and objective weights of each indicator in the index layer of the evaluation system. Thirdly, the improved D-S evidence fusion algorithm is used to synthesize multi-source evidence to obtain the indicator weights, so as to avoid the one-sidedness of a single assignment and get the optimal comprehensive weight. Finally, the comprehensive evaluation index of the construction site is calculated according to the TOPSIS evaluation algorithm. The analysis shows that the safety risk assessment model based on the improved D-S evidence theory and fusion weight set can effectively assess the safety of construction sites, reduce the uncertainty of assessment results, and improve the credibility of risk assessment results.
Available online: May 12, 2023 , DOI: 10.15888/j.cnki.csa.009119
Abstract:
Source code summarization is designed to automatically generate precise summarization for natural language, so as to help developers better understand and maintain source code. Traditional research methods generate source code summaries by using information retrieval techniques, which select corresponding words from the original source code or adapt summaries of similar code snippets; recent research adopts machine translation methods and generates summaries of code snippets by selecting the encoder-decoder neural network model. However, there are two main problems in existing summarization generation methods: on the one hand, the neural network-based method is more friendly to the high-frequency words appearing in the code snippets, but it tends to weaken the processing of low-frequency words; on the other hand, programming languages ??are highly structured, so source code cannot simply be treated as serialized text, or otherwise, it will lead to loss of contextual structure information. Therefore, in order to solve the problem of low-frequency words, a retrieval-based neural machine translation approach is proposed. Similar code snippets retrieved from the training set are used to enhance the neural network model. In addition, to learn the structured semantic information of code snippets, this study proposes a structured-guided Transformer, which encodes structural information of codes through an attention mechanism. The experimental results show that the model has significant advantages over the deep learning model generated by the current cutting-edge code summarization in processing low-frequency words and structured semantics.
Source code summarization is designed to automatically generate precise summarization for natural language, so as to help developers better understand and maintain source code. Traditional research methods generate source code summaries by using information retrieval techniques, which select corresponding words from the original source code or adapt summaries of similar code snippets; recent research adopts machine translation methods and generates summaries of code snippets by selecting the encoder-decoder neural network model. However, there are two main problems in existing summarization generation methods: on the one hand, the neural network-based method is more friendly to the high-frequency words appearing in the code snippets, but it tends to weaken the processing of low-frequency words; on the other hand, programming languages ??are highly structured, so source code cannot simply be treated as serialized text, or otherwise, it will lead to loss of contextual structure information. Therefore, in order to solve the problem of low-frequency words, a retrieval-based neural machine translation approach is proposed. Similar code snippets retrieved from the training set are used to enhance the neural network model. In addition, to learn the structured semantic information of code snippets, this study proposes a structured-guided Transformer, which encodes structural information of codes through an attention mechanism. The experimental results show that the model has significant advantages over the deep learning model generated by the current cutting-edge code summarization in processing low-frequency words and structured semantics.
Available online: April 28, 2023 , DOI: 10.15888/j.cnki.csa.009178
Abstract:
In the Sloan digital sky survey (SDSS), the current object detection algorithm is inefficient in the detection of small-scale astronomical objects due to interference from large and bright astronomical objects. To address this issue, a small-scale astronomical object detection method based on Mask-GAN and improved YOLOv3 is proposed. The method is executed in two steps. The first step is to mask the interfering astronomical objects. A Mask construction algorithm for interfering astronomical objects is designed, which extracts the interfering objects by adaptive threshold segmentation and connectivity domain analysis, and the Mask is constructed by the method of fusing the features of band regions to avoid halo residue and excluding adjacent objects to avoid segmentation errors. Then, a GAN model is built, which is combined with the Mask of interfering astronomical objects to complete the interference masking task. The second step is to input the processed data into the improved YOLOv3 model for small-scale astronomical object detection. C-EfficientNet with an attention mechanism is built as the backbone network of the improved YOLOv3 to strengthen the feature extraction capability and increase the network’s attention to objects. Meanwhile, four effective feature layers are extended, and the method SAt is proposed to increase the weight of shallow feature maps so that the network can better use high-resolution shallow features with more details to detect small-scale astronomical objects. Experiments and analysis show that the average accuracy of the method in detecting small-scale stars and galaxies on the SDSS astronomical dataset reaches 81.16% and 77.89%, respectively, The proposed detection method is better than the classic one and is of certain practical application significance.
In the Sloan digital sky survey (SDSS), the current object detection algorithm is inefficient in the detection of small-scale astronomical objects due to interference from large and bright astronomical objects. To address this issue, a small-scale astronomical object detection method based on Mask-GAN and improved YOLOv3 is proposed. The method is executed in two steps. The first step is to mask the interfering astronomical objects. A Mask construction algorithm for interfering astronomical objects is designed, which extracts the interfering objects by adaptive threshold segmentation and connectivity domain analysis, and the Mask is constructed by the method of fusing the features of band regions to avoid halo residue and excluding adjacent objects to avoid segmentation errors. Then, a GAN model is built, which is combined with the Mask of interfering astronomical objects to complete the interference masking task. The second step is to input the processed data into the improved YOLOv3 model for small-scale astronomical object detection. C-EfficientNet with an attention mechanism is built as the backbone network of the improved YOLOv3 to strengthen the feature extraction capability and increase the network’s attention to objects. Meanwhile, four effective feature layers are extended, and the method SAt is proposed to increase the weight of shallow feature maps so that the network can better use high-resolution shallow features with more details to detect small-scale astronomical objects. Experiments and analysis show that the average accuracy of the method in detecting small-scale stars and galaxies on the SDSS astronomical dataset reaches 81.16% and 77.89%, respectively, The proposed detection method is better than the classic one and is of certain practical application significance.
Available online: April 28, 2023 , DOI: 10.15888/j.cnki.csa.009179
Abstract:
As YOLOv3, an algorithm widely used in the field of remote sensing target detection, has insufficient feature expression ability for small targets and a poor detection effect, an improved YOLOv3 algorithm for small target detection is proposed. Firstly, the global context (GC) attention mechanism is introduced, and the feature extraction network and feature pyramid networks (FPN) are improved to enhance the small-target feature extraction ability and detection ability of the model. Secondly, single-scale Retinex (SSR) fusion feature enhancement is applied to the dataset to improve the model’s learning effect of small target features. Finally, the adaptive anchor box optimization (AABO) algorithm is adopted to optimize anchors and better match anchors and targets. The experimental results on the remote sensing dataset RSOD show that the mean average precision (mAP) of the proposed algorithm is 92.5%, which is improved by 10.1% compared with that of the classic YOLOv3 algorithm, and the detection effect of small remote sensing targets is significantly improved.
As YOLOv3, an algorithm widely used in the field of remote sensing target detection, has insufficient feature expression ability for small targets and a poor detection effect, an improved YOLOv3 algorithm for small target detection is proposed. Firstly, the global context (GC) attention mechanism is introduced, and the feature extraction network and feature pyramid networks (FPN) are improved to enhance the small-target feature extraction ability and detection ability of the model. Secondly, single-scale Retinex (SSR) fusion feature enhancement is applied to the dataset to improve the model’s learning effect of small target features. Finally, the adaptive anchor box optimization (AABO) algorithm is adopted to optimize anchors and better match anchors and targets. The experimental results on the remote sensing dataset RSOD show that the mean average precision (mAP) of the proposed algorithm is 92.5%, which is improved by 10.1% compared with that of the classic YOLOv3 algorithm, and the detection effect of small remote sensing targets is significantly improved.
Available online: April 28, 2023 , DOI: 10.15888/j.cnki.csa.009170
Abstract:
Due to the differences between development teams and the complexity, uncertainty, and dynamics of development projects, it is difficult to reasonably allocate development tasks. Considering the factors such as the uncertain capability of development teams and the uncertain operation process of development projects, the duration and cost of development projects are evaluated through simulation. The psychological factors of decision-makers are taken into account, and the prospect value of development duration and costs is calculated by the prospect theory. After that, the prospect value is taken as the fitness evaluation index, and a task-allocation optimization algorithm is constructed on the basis of NSGA-III. The case study shows that the optimization algorithms based on NSGA-III, NSGA-II, and MOEA-D can all effectively improve the allocation scheme of development tasks, and the optimization based on NSGA-III is the best.
Due to the differences between development teams and the complexity, uncertainty, and dynamics of development projects, it is difficult to reasonably allocate development tasks. Considering the factors such as the uncertain capability of development teams and the uncertain operation process of development projects, the duration and cost of development projects are evaluated through simulation. The psychological factors of decision-makers are taken into account, and the prospect value of development duration and costs is calculated by the prospect theory. After that, the prospect value is taken as the fitness evaluation index, and a task-allocation optimization algorithm is constructed on the basis of NSGA-III. The case study shows that the optimization algorithms based on NSGA-III, NSGA-II, and MOEA-D can all effectively improve the allocation scheme of development tasks, and the optimization based on NSGA-III is the best.
Available online: April 28, 2023 , DOI: 10.15888/j.cnki.csa.009171
Abstract:
An intelligent robot depalletizing system based on visual positioning is designed to solve the problem that the traditional teaching and playback robot can only perform depalletizing tasks with given positions and fixed trajectories and thus is limited to fixed scenes. The system uses the coordinate transformation of the target pixel center to obtain the corresponding world coordinates. For the problem that the eye-in-hand camera may lead to the inaccurate rotation angle of the target obtained by the image processing algorithm due to the deflection of the camera, it is proposed to use the extrinsic parameter coefficient of the camera to compensate for the rotation angle of the target. Moreover, a depalletization strategy is designed, and the communication guides the robot to automatically perform the depalletization task by grabbing from nearest to farthest without manual intervention. The experimental data shows that the system can grab the target with an unknown position in an unknown work scene, with a position error of 1.1 mm and an angle error of 1.2°, and the time to position the stacking layer is about 1.2 s. The system meets precision and efficiency requirements for depalletizing robots in the industrial scenes.
An intelligent robot depalletizing system based on visual positioning is designed to solve the problem that the traditional teaching and playback robot can only perform depalletizing tasks with given positions and fixed trajectories and thus is limited to fixed scenes. The system uses the coordinate transformation of the target pixel center to obtain the corresponding world coordinates. For the problem that the eye-in-hand camera may lead to the inaccurate rotation angle of the target obtained by the image processing algorithm due to the deflection of the camera, it is proposed to use the extrinsic parameter coefficient of the camera to compensate for the rotation angle of the target. Moreover, a depalletization strategy is designed, and the communication guides the robot to automatically perform the depalletization task by grabbing from nearest to farthest without manual intervention. The experimental data shows that the system can grab the target with an unknown position in an unknown work scene, with a position error of 1.1 mm and an angle error of 1.2°, and the time to position the stacking layer is about 1.2 s. The system meets precision and efficiency requirements for depalletizing robots in the industrial scenes.
Available online: April 28, 2023 , DOI: 10.15888/j.cnki.csa.009112
Abstract:
Human pose estimation based on deep learning is widely used in pose recognition, human-computer interaction, and other fields. In order to improve the detection accuracy of key points of the human body, many networks adopt a model architecture with increasing calculation amount, parameter amount, and complexity, which is impossible to be directly deployed to low-computing devices. To solve the above issues, this study proposes a lightweight method for multi-branch feature attention fusion. The model is based on the HigherHRNet network for lightweight design and training. Specifically, channel splitting and channel shuffling are adopted to solve the information isolation between feature layers after group convolution; the feature generation method of linear operation is used to address the redundancy between different feature layers; the method of fusing attention information is employed to alleviate the accuracy drop caused by lightweight. The training, testing, visualization, and ablation experiments of the model are completed on the MS COCO dataset. The experimental results show that the lightweight method in this study can significantly reduce the calculation amount of human pose estimation under the premise of ensuring intuitive detection accuracy.
Human pose estimation based on deep learning is widely used in pose recognition, human-computer interaction, and other fields. In order to improve the detection accuracy of key points of the human body, many networks adopt a model architecture with increasing calculation amount, parameter amount, and complexity, which is impossible to be directly deployed to low-computing devices. To solve the above issues, this study proposes a lightweight method for multi-branch feature attention fusion. The model is based on the HigherHRNet network for lightweight design and training. Specifically, channel splitting and channel shuffling are adopted to solve the information isolation between feature layers after group convolution; the feature generation method of linear operation is used to address the redundancy between different feature layers; the method of fusing attention information is employed to alleviate the accuracy drop caused by lightweight. The training, testing, visualization, and ablation experiments of the model are completed on the MS COCO dataset. The experimental results show that the lightweight method in this study can significantly reduce the calculation amount of human pose estimation under the premise of ensuring intuitive detection accuracy.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.008950
Abstract:
Considering the high cost and poor timeliness of manual testing and the insufficient scalability of conventional automatic interface testing tools, an interface test platform (OLa) that supports the parallel testing of test case sets is proposed in this study. OLa is divided into the user presentation layer, application logic layer, data service layer, and test case execution layer according to a layered architecture model. Among them, the user presentation layer is developed on the basis of the Vue framework, which is combined with Vue Router, Vuex, and other tools to realize the single-page application. The application logic layer is realized with the Spring Boot framework, and the data service layer is based on the MyBatis-Plus framework and Spring Data framework. The test case execution layer uses okhttp3, fastjson, Jackson, and other tools to implement interface testing. In addition, according to the ideas of the technical architecture of systems, Java network programming, and abstraction-oriented programming, this study innovatively proposes the test case execution process with the C/S architecture and the automatic matching verification method based on parameter identification, which can solve the problem that some traditional automatic testing tools cannot support parallel testing. The experimental results show that OLa can support test case testing, serial testing, and parallel testing of test case sets and can automatically identify test case parameters and verify the interface response content, which improves the flexibility and effectiveness of interface testing. Moreover, it can reduce the difficulty of interface testing and improve the efficiency of interface testing without interdependence between test cases.
Considering the high cost and poor timeliness of manual testing and the insufficient scalability of conventional automatic interface testing tools, an interface test platform (OLa) that supports the parallel testing of test case sets is proposed in this study. OLa is divided into the user presentation layer, application logic layer, data service layer, and test case execution layer according to a layered architecture model. Among them, the user presentation layer is developed on the basis of the Vue framework, which is combined with Vue Router, Vuex, and other tools to realize the single-page application. The application logic layer is realized with the Spring Boot framework, and the data service layer is based on the MyBatis-Plus framework and Spring Data framework. The test case execution layer uses okhttp3, fastjson, Jackson, and other tools to implement interface testing. In addition, according to the ideas of the technical architecture of systems, Java network programming, and abstraction-oriented programming, this study innovatively proposes the test case execution process with the C/S architecture and the automatic matching verification method based on parameter identification, which can solve the problem that some traditional automatic testing tools cannot support parallel testing. The experimental results show that OLa can support test case testing, serial testing, and parallel testing of test case sets and can automatically identify test case parameters and verify the interface response content, which improves the flexibility and effectiveness of interface testing. Moreover, it can reduce the difficulty of interface testing and improve the efficiency of interface testing without interdependence between test cases.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.009080
Abstract:
During the classification of Alzheimer’s disease, the hypergraph neural network (HGNN) can extract features from the hypergraph relationship between subjects, which has a good advantage in representing and learning the structure of complex graphs. However, most models directly or indirectly decompose the higher-order complex relationship between subjects represented by hypergraphs into the simple binary relationship for feature learning, without effectively using the higher-order information of hyperedges. Therefore, an Alzheimer’s disease classification model based on the line-hypergraph neural network (L-HGNN) is proposed. The model uses sparse linear regression to represent the multiple correlations between subjects. With the help of the transformation of hypergraphs and line graphs, the higher-order neighborhood information transmission of nodes and the learning of overall structural features of hyperedges are realized in convolutional network models. Meanwhile, a more differentiated node embedding is generated by the attention mechanism, which is then used in the auxiliary diagnosis of Alzheimer’s disease. Compared with the results of two commonly used methods on the ADNI dataset, the experimental results show that the proposed method can effectively improve the classification accuracy and has important application value in the early diagnosis of Alzheimer’s disease.
During the classification of Alzheimer’s disease, the hypergraph neural network (HGNN) can extract features from the hypergraph relationship between subjects, which has a good advantage in representing and learning the structure of complex graphs. However, most models directly or indirectly decompose the higher-order complex relationship between subjects represented by hypergraphs into the simple binary relationship for feature learning, without effectively using the higher-order information of hyperedges. Therefore, an Alzheimer’s disease classification model based on the line-hypergraph neural network (L-HGNN) is proposed. The model uses sparse linear regression to represent the multiple correlations between subjects. With the help of the transformation of hypergraphs and line graphs, the higher-order neighborhood information transmission of nodes and the learning of overall structural features of hyperedges are realized in convolutional network models. Meanwhile, a more differentiated node embedding is generated by the attention mechanism, which is then used in the auxiliary diagnosis of Alzheimer’s disease. Compared with the results of two commonly used methods on the ADNI dataset, the experimental results show that the proposed method can effectively improve the classification accuracy and has important application value in the early diagnosis of Alzheimer’s disease.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.009121
Abstract:
The rooting algorithms, image segmentation in computer vision, and many problems in machine learning can be regarded as problems seeking solutions to the maximum flow of networks. For more efficient maximum flow algorithms based on hierarchical networks, a maximum flow algorithm based on a memory-aided search strategy is put forward. The traditional Edmonds-Karp algorithm and Dinic’s algorithm suffer from extra overhead due to repeated searches of invalid paths. Hence, a memory-aided search strategy that can record search states is proposed to conquer this problem. Experimental results show that the proposed strategy is efficient and feasible, and the proposed algorithm outperforms Dinic’s algorithm.
The rooting algorithms, image segmentation in computer vision, and many problems in machine learning can be regarded as problems seeking solutions to the maximum flow of networks. For more efficient maximum flow algorithms based on hierarchical networks, a maximum flow algorithm based on a memory-aided search strategy is put forward. The traditional Edmonds-Karp algorithm and Dinic’s algorithm suffer from extra overhead due to repeated searches of invalid paths. Hence, a memory-aided search strategy that can record search states is proposed to conquer this problem. Experimental results show that the proposed strategy is efficient and feasible, and the proposed algorithm outperforms Dinic’s algorithm.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.009069
Abstract:
Economic globalization has given logo a huge commercial value, and the development of the computer vision provides a broader application field for logo classification and recognition. This study considers that the overall features of logo images are not significant, and the number of images is large, and then it proposes progressive multi-granularity training of jigsaw patches (PMG-Net), a method of fine-grained image classification, to classify the logo image dataset, so as to improve the ability of the model to classify logo images. The input images containing different granularity information are generated by the jigsaw patch generator, and then the progressive multi-granularity training module is introduced to fuse the features of different granularities. The fused features pay more attention to the subtle differences between images so that the effect of logo image classification is significantly improved. The leaky rectified linear unit (LeakyReLU) activation function is used to retain the negative feature information in the image when the input image features are extracted, and the channel attention mechanism is introduced to adjust the weights of the feature channels, so as to enhance the feature information guiding ability and improve the classification effect of the model. The experimental results show that the classification accuracy of this study on the logo image dataset is better than that of traditional classification methods. This study achieves efficient classification of logo images by incorporating a progressive training strategy with multi-granularity features and a random jigsaw patch generator, which provides a new idea to solve the existing problems in logo image classification.
Economic globalization has given logo a huge commercial value, and the development of the computer vision provides a broader application field for logo classification and recognition. This study considers that the overall features of logo images are not significant, and the number of images is large, and then it proposes progressive multi-granularity training of jigsaw patches (PMG-Net), a method of fine-grained image classification, to classify the logo image dataset, so as to improve the ability of the model to classify logo images. The input images containing different granularity information are generated by the jigsaw patch generator, and then the progressive multi-granularity training module is introduced to fuse the features of different granularities. The fused features pay more attention to the subtle differences between images so that the effect of logo image classification is significantly improved. The leaky rectified linear unit (LeakyReLU) activation function is used to retain the negative feature information in the image when the input image features are extracted, and the channel attention mechanism is introduced to adjust the weights of the feature channels, so as to enhance the feature information guiding ability and improve the classification effect of the model. The experimental results show that the classification accuracy of this study on the logo image dataset is better than that of traditional classification methods. This study achieves efficient classification of logo images by incorporating a progressive training strategy with multi-granularity features and a random jigsaw patch generator, which provides a new idea to solve the existing problems in logo image classification.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.009150
Abstract:
High-quality question-answering plays an important role in human activities and artificial intelligence because it can help to obtain knowledge from articles, improve the performance of question-answering systems, and promote machine reading comprehension. The current mainstream question-answer pair generation methods usually rely on candidate answers in the provided article to generate specific questions based on these answers. However, some candidate answers may generate questions that cannot be answered from the article, or the answers to the generated questions are no longer the same as the candidate answers, which thus results in a poor correlation of the question-answer pairs and affects the quality of the question-answer pairs. In order to solve these problems, this study proposes a method to generate question-answer pairs based on key phrase extraction and filtering. The method can automatically extract key phrases suitable for generating questions from the input text as the candidate answers and then generate question-answer pairs by a question generator and an answer generator according to the candidate answers. Finally, the method outputs question-answer pairs with high quality by comparing the similarity between the candidate answers and the generated answers and filtering out those question-answer pairs that have a low correlation with the candidate answers. The proposed method is evaluated by experiments on SQUAD1.1 and NewsQA datasets, and the quality of generated question-answer pairs is manually checked. The results show that this method can effectively improve the quality of generated question-answer pairs.
High-quality question-answering plays an important role in human activities and artificial intelligence because it can help to obtain knowledge from articles, improve the performance of question-answering systems, and promote machine reading comprehension. The current mainstream question-answer pair generation methods usually rely on candidate answers in the provided article to generate specific questions based on these answers. However, some candidate answers may generate questions that cannot be answered from the article, or the answers to the generated questions are no longer the same as the candidate answers, which thus results in a poor correlation of the question-answer pairs and affects the quality of the question-answer pairs. In order to solve these problems, this study proposes a method to generate question-answer pairs based on key phrase extraction and filtering. The method can automatically extract key phrases suitable for generating questions from the input text as the candidate answers and then generate question-answer pairs by a question generator and an answer generator according to the candidate answers. Finally, the method outputs question-answer pairs with high quality by comparing the similarity between the candidate answers and the generated answers and filtering out those question-answer pairs that have a low correlation with the candidate answers. The proposed method is evaluated by experiments on SQUAD1.1 and NewsQA datasets, and the quality of generated question-answer pairs is manually checked. The results show that this method can effectively improve the quality of generated question-answer pairs.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.009151
Abstract:
Monkeypox virus is currently circulating globally and is clinically indistinguishable from other skin diseases, particularly the smallpox virus and chickenpox virus. In the case that deterministic polymerase chain reaction technology and other biological detection technologies are not fully mature, it is a feasible method to detect skin lesions caused by the monkeypox virus by computer-aided diagnostic technology, so a classification algorithm for skin lesions caused by the monkeypox virus based on the residual network is proposed. Based on the residual network, the algorithm combines deep separable convolution and lightweight attention, which reduces the computational amount and complexity of the model and improves the classification performance of the model. The experimental results show that the algorithm shows excellent classification performance for skin lesions caused by the monkeypox virus, and the classification accuracy, recall, and precision of skin lesions caused by the monkeypox virus are 97.3%, 96.8%, and 97.2%, respectively, which are better than those of the common classification models and other research methods used in the experiment.
Monkeypox virus is currently circulating globally and is clinically indistinguishable from other skin diseases, particularly the smallpox virus and chickenpox virus. In the case that deterministic polymerase chain reaction technology and other biological detection technologies are not fully mature, it is a feasible method to detect skin lesions caused by the monkeypox virus by computer-aided diagnostic technology, so a classification algorithm for skin lesions caused by the monkeypox virus based on the residual network is proposed. Based on the residual network, the algorithm combines deep separable convolution and lightweight attention, which reduces the computational amount and complexity of the model and improves the classification performance of the model. The experimental results show that the algorithm shows excellent classification performance for skin lesions caused by the monkeypox virus, and the classification accuracy, recall, and precision of skin lesions caused by the monkeypox virus are 97.3%, 96.8%, and 97.2%, respectively, which are better than those of the common classification models and other research methods used in the experiment.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.009145
Abstract:
As an extension of software services in the information world to the real world through the Internet of Things (IoT), IoT services have an important role in IoT systems. However, unlike traditional Web services, IoT services have new characteristics such as reality perception, data driving, heterogeneous distribution, and spatio-temporal correlation, which make the existing service models insufficient for an effective portrayal of IoT services and fail to meet the requirements of subsequent service discovery, service offloading, and service combination in IoT applications. On the basis of condensed analysis of IoT service modeling requirements and existing IoT service models, an entity-data-based IoT service modeling framework is proposed, which puts forward the concept of IoT service models fusing ternary information of service, entity, and data and their conceptual relationships. In addition, the framework focuses on defining the spatio-temporal attributes and dependencies of service, entity, and data to support IoT service association representation and analysis based on spatio-temporal correlation, and the entity-data-based IoT service description method is given by extending ontology Web language for services (OWL-S). Finally, the usage mode and effects of the model are discussed in the context of a highway IoT application case.
As an extension of software services in the information world to the real world through the Internet of Things (IoT), IoT services have an important role in IoT systems. However, unlike traditional Web services, IoT services have new characteristics such as reality perception, data driving, heterogeneous distribution, and spatio-temporal correlation, which make the existing service models insufficient for an effective portrayal of IoT services and fail to meet the requirements of subsequent service discovery, service offloading, and service combination in IoT applications. On the basis of condensed analysis of IoT service modeling requirements and existing IoT service models, an entity-data-based IoT service modeling framework is proposed, which puts forward the concept of IoT service models fusing ternary information of service, entity, and data and their conceptual relationships. In addition, the framework focuses on defining the spatio-temporal attributes and dependencies of service, entity, and data to support IoT service association representation and analysis based on spatio-temporal correlation, and the entity-data-based IoT service description method is given by extending ontology Web language for services (OWL-S). Finally, the usage mode and effects of the model are discussed in the context of a highway IoT application case.
Available online: April 25, 2023 , DOI: 10.15888/j.cnki.csa.009148
Abstract:
Multi-label image classification is a research hotspot in multi-label data classification. The existing multi-label image classification methods only learn the visual representation features of images and ignore the relevant information between image labels and the correspondence between label semantics and image features. In order to solve these problems, a multi-label image classification model based on a multi-head graph attention network and graph model (ML-M-GAT) is proposed. By using label co-occurrence and attribute information, the model builds a graph model, and it employs the multi-head attention mechanism to learn the attention weight of the label. In addition, the model utilizes label weights to fuse label semantic features and image features, so as to integrate label correlation and label semantic information into the multi-label image classification model. In order to verify the effectiveness of the proposed model, experiments are carried out on the public datasets VOC-2007 and COCO-2014, and the experimental results show that the average mean accuracy (mAP) of the ML-M-GAT model on the two datasets is 94% and 82.2%, respectively, which are better than that of CNN-RNN, ResNet101, MLIR, and MIC-FLC models and are 4.2% and 3.9% higher than that of ResNet101 models, respectively. Therefore, the proposed model can improve the performance of multi-label image classification by using image label information.
Multi-label image classification is a research hotspot in multi-label data classification. The existing multi-label image classification methods only learn the visual representation features of images and ignore the relevant information between image labels and the correspondence between label semantics and image features. In order to solve these problems, a multi-label image classification model based on a multi-head graph attention network and graph model (ML-M-GAT) is proposed. By using label co-occurrence and attribute information, the model builds a graph model, and it employs the multi-head attention mechanism to learn the attention weight of the label. In addition, the model utilizes label weights to fuse label semantic features and image features, so as to integrate label correlation and label semantic information into the multi-label image classification model. In order to verify the effectiveness of the proposed model, experiments are carried out on the public datasets VOC-2007 and COCO-2014, and the experimental results show that the average mean accuracy (mAP) of the ML-M-GAT model on the two datasets is 94% and 82.2%, respectively, which are better than that of CNN-RNN, ResNet101, MLIR, and MIC-FLC models and are 4.2% and 3.9% higher than that of ResNet101 models, respectively. Therefore, the proposed model can improve the performance of multi-label image classification by using image label information.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009149
Abstract:
To address the low accuracy of underwater fish target detection caused by blurred and color-distorted underwater images, complex underwater scenes, and limited target feature extraction ability, this study proposes an improved underwater fish target detection algorithm based on YOLOv5. Firstly, in response to the blurring and color distortion of underwater images, the underwater dark channel prior (UDCP) algorithm is introduced to pre-process the images, which is helpful for correctly identifying the target in different environments. Then, considering the problems of complex underwater scenes and limited target feature extraction ability, the study introduces an efficient correlation channel, i.e., efficient channel attention (ECA), into the YOLOv5 network to enhance the feature extraction ability of the target. Finally, the loss function is improved to enhance the accuracy of the target detection box. Experiments show that the accuracy of the improved YOLOv5 in underwater fish target detection is 2.95% higher than that of the original YOLOv5, and the average detection accuracy (mAP@0.5:0.95) is increased by 5.52%.
To address the low accuracy of underwater fish target detection caused by blurred and color-distorted underwater images, complex underwater scenes, and limited target feature extraction ability, this study proposes an improved underwater fish target detection algorithm based on YOLOv5. Firstly, in response to the blurring and color distortion of underwater images, the underwater dark channel prior (UDCP) algorithm is introduced to pre-process the images, which is helpful for correctly identifying the target in different environments. Then, considering the problems of complex underwater scenes and limited target feature extraction ability, the study introduces an efficient correlation channel, i.e., efficient channel attention (ECA), into the YOLOv5 network to enhance the feature extraction ability of the target. Finally, the loss function is improved to enhance the accuracy of the target detection box. Experiments show that the accuracy of the improved YOLOv5 in underwater fish target detection is 2.95% higher than that of the original YOLOv5, and the average detection accuracy (mAP@0.5:0.95) is increased by 5.52%.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009144
Abstract:
Due to the complexity of social media networks, the classification of social media accounts by mono-nature homogeneous information networks causes information loss and has a negative impact on the classification results. To solve this problem, this study proposes a social media account classification method based on heterogeneous graph convolutional attention networks (HGCANA). Specifically, a heterogeneous information network of social media is constructed, and the social media features of the network are extracted. After that, the attention mechanism is introduced to classify and identify social media accounts. The HGCANA method is compared with the existing methods through experiments, and it is proved that the HGCANA method registers better performance in the effective classification of social media accounts.
Due to the complexity of social media networks, the classification of social media accounts by mono-nature homogeneous information networks causes information loss and has a negative impact on the classification results. To solve this problem, this study proposes a social media account classification method based on heterogeneous graph convolutional attention networks (HGCANA). Specifically, a heterogeneous information network of social media is constructed, and the social media features of the network are extracted. After that, the attention mechanism is introduced to classify and identify social media accounts. The HGCANA method is compared with the existing methods through experiments, and it is proved that the HGCANA method registers better performance in the effective classification of social media accounts.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009158
Abstract:
Encryption and dynamic port technology make the traditional traffic classification technology fail to meet the performance requirements of online game identification. In this study, an end-to-end traffic classification model based on auto-encoder dimension reduction is proposed to accurately identify online game traffic. First, the original traffic is preprocessed into a one-dimensional session flow quantity of 784 B, and the encoder is used for unsupervised dimension reduction and removing invalid features. Then, the parallel algorithm of the convolutional neural network and LSTM network is explored and constructed to extract and fuse spatial and temporal features of samples after dimension reduction. Finally, the fusion features are used for classification. When tested on the self-built game traffic dataset and the open dataset, the proposed model achieves an accuracy rate of 97.68% in online game traffic identification. Compared with the traditional end-to-end network traffic classification model, the model designed in this study is more lightweight and practical and can be easily deployed on devices with limited resources.
Encryption and dynamic port technology make the traditional traffic classification technology fail to meet the performance requirements of online game identification. In this study, an end-to-end traffic classification model based on auto-encoder dimension reduction is proposed to accurately identify online game traffic. First, the original traffic is preprocessed into a one-dimensional session flow quantity of 784 B, and the encoder is used for unsupervised dimension reduction and removing invalid features. Then, the parallel algorithm of the convolutional neural network and LSTM network is explored and constructed to extract and fuse spatial and temporal features of samples after dimension reduction. Finally, the fusion features are used for classification. When tested on the self-built game traffic dataset and the open dataset, the proposed model achieves an accuracy rate of 97.68% in online game traffic identification. Compared with the traditional end-to-end network traffic classification model, the model designed in this study is more lightweight and practical and can be easily deployed on devices with limited resources.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009161
Abstract:
In view of the flash point prediction of constant line aviation kerosene, a soft sensor method based on the grey correlation analysis (GRA) and improved whale optimization algorithm (IWOA) is proposed to optimize the extreme learning machine (ELM). GRA is used to calculate the information correlation degree between each auxiliary variable and the variable to be tested. Auxiliary variables are selected as inputs through the experimental method, and then IWOA is used to find the optimal weight threshold for ELM. In the early stage of the algorithm iteration, the improved Tent chaotic mapping is used to initialize the population to make the population distribution more uniform. The adaptive weight is combined with a random difference variation strategy to improve the optimization ability of the algorithm. The effectiveness of the improved algorithm is verified by eight benchmark test functions, and the improved model is proven to be effective in predicting flash points by the actual flash point data of the constant line aviation kerosene in an atmospheric tower of a refinery.
In view of the flash point prediction of constant line aviation kerosene, a soft sensor method based on the grey correlation analysis (GRA) and improved whale optimization algorithm (IWOA) is proposed to optimize the extreme learning machine (ELM). GRA is used to calculate the information correlation degree between each auxiliary variable and the variable to be tested. Auxiliary variables are selected as inputs through the experimental method, and then IWOA is used to find the optimal weight threshold for ELM. In the early stage of the algorithm iteration, the improved Tent chaotic mapping is used to initialize the population to make the population distribution more uniform. The adaptive weight is combined with a random difference variation strategy to improve the optimization ability of the algorithm. The effectiveness of the improved algorithm is verified by eight benchmark test functions, and the improved model is proven to be effective in predicting flash points by the actual flash point data of the constant line aviation kerosene in an atmospheric tower of a refinery.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009165
Abstract:
The existing traditional semantic segmentation methods of crop diseases have low accuracy and poor robustness. In order to address these problems, an improved UNet semantic segmentation model of strawberry diseases based on an attention mechanism is proposed. Firstly, a CNN-Transformer hybrid structure is added to the encoder to improve the feature extraction ability of global information and local detail information. Secondly, the traditional up-sampling is replaced by a dual up-sample module in the decoder to enhance the feature extraction ability and segmentation accuracy. Thirdly, the hard-swish activation function is employed to replace the ReLU activation function, and the smoother curve helps to improve generalization and nonlinear feature extraction ability and prevent gradient disappearance. Finally, the segmentation accuracy is further improved by using a combined cross-entropy Dice loss function to strengthen the model’s constraints on the segmentation results. A dataset consisting of 2 500 images of seven strawberry diseases is used to segment strawberry diseases in a complex background. The semantic segmentation pixel accuracy reaches 92.56%, and the average cross-merge ratio reaches 84.97%. The experimental results show that the improved UNet in this study can achieve better segmentation results and outperform most segmentation models in the semantic segmentation of strawberry diseases.
The existing traditional semantic segmentation methods of crop diseases have low accuracy and poor robustness. In order to address these problems, an improved UNet semantic segmentation model of strawberry diseases based on an attention mechanism is proposed. Firstly, a CNN-Transformer hybrid structure is added to the encoder to improve the feature extraction ability of global information and local detail information. Secondly, the traditional up-sampling is replaced by a dual up-sample module in the decoder to enhance the feature extraction ability and segmentation accuracy. Thirdly, the hard-swish activation function is employed to replace the ReLU activation function, and the smoother curve helps to improve generalization and nonlinear feature extraction ability and prevent gradient disappearance. Finally, the segmentation accuracy is further improved by using a combined cross-entropy Dice loss function to strengthen the model’s constraints on the segmentation results. A dataset consisting of 2 500 images of seven strawberry diseases is used to segment strawberry diseases in a complex background. The semantic segmentation pixel accuracy reaches 92.56%, and the average cross-merge ratio reaches 84.97%. The experimental results show that the improved UNet in this study can achieve better segmentation results and outperform most segmentation models in the semantic segmentation of strawberry diseases.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009166
Abstract:
Graph neural networks have achieved remarkable performance in semi-supervised node classification tasks. Relevant research has shown that graph neural networks are susceptible to perturbations, and there is research studying the adversarial robustness of graph neural networks. However, gradient-based attacks cannot guarantee optimal perturbation. Therefore, an adversarial attack method based on gradient and structure is proposed to enhance the gradient-based perturbation. The method first generates candidate perturbation sets by using first-order optimization of training losses, and then it evaluates the similarity of the candidate sets. Finally, it ranks them according to the evaluation results and selects a fixed-budget modification to achieve the attack. The proposed attack method is evaluated by performing a semi-supervised node classification task on five datasets. Experimental results show that the node classification accuracy decreases significantly when only a small number of perturbations are performed, which indicates that the proposed method significantly outperforms the existing attack methods.
Graph neural networks have achieved remarkable performance in semi-supervised node classification tasks. Relevant research has shown that graph neural networks are susceptible to perturbations, and there is research studying the adversarial robustness of graph neural networks. However, gradient-based attacks cannot guarantee optimal perturbation. Therefore, an adversarial attack method based on gradient and structure is proposed to enhance the gradient-based perturbation. The method first generates candidate perturbation sets by using first-order optimization of training losses, and then it evaluates the similarity of the candidate sets. Finally, it ranks them according to the evaluation results and selects a fixed-budget modification to achieve the attack. The proposed attack method is evaluated by performing a semi-supervised node classification task on five datasets. Experimental results show that the node classification accuracy decreases significantly when only a small number of perturbations are performed, which indicates that the proposed method significantly outperforms the existing attack methods.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009124
Abstract:
Formal methods are required for the automatic generation of codes to ensure that the code generated by the compiler can be applied to nuclear power instrument and control systems and thus minimize the errors introduced by the compiler during the compilation of synchronous data-flow languages. This study uses the theorem proving tool Coq to formally define the syntax, semantics, and translation algorithms involved in the translation phase of the master-node input structure of the synchronous data-flow language from Lustre to Clight and completes the formal proof of the translation algorithm. It is shown that this formalized compiler can generate credible target code that is consistent with the behavior of the source code, and meanwhile, the generated target code can well satisfy the implementation specifications of nuclear power instrument and control systems.
Formal methods are required for the automatic generation of codes to ensure that the code generated by the compiler can be applied to nuclear power instrument and control systems and thus minimize the errors introduced by the compiler during the compilation of synchronous data-flow languages. This study uses the theorem proving tool Coq to formally define the syntax, semantics, and translation algorithms involved in the translation phase of the master-node input structure of the synchronous data-flow language from Lustre to Clight and completes the formal proof of the translation algorithm. It is shown that this formalized compiler can generate credible target code that is consistent with the behavior of the source code, and meanwhile, the generated target code can well satisfy the implementation specifications of nuclear power instrument and control systems.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009128
Abstract:
For the passive location of radiation sources for motion communications in complex environments, the closed-form solution method is sensitive to measurement noise in time–frequency difference models and has a large root-mean-square error of location. To improve the location performance under large observation errors, this study proposes a recursive hybrid TDOA/FDOA location method, which is based on weighted least squares and the genetic algorithm. Firstly, massive time–frequency difference data are observed at known stations, and error models are built. On this basis of the models, multiple sets of time–frequency difference sequences are processed. Secondly, the initial value of the target position is solved by weighted least squares. Given the initial value, the improved genetic algorithm is used to solve and correct the position coordinates through multiple groups of time–frequency difference sequences iteratively and recursively. Finally, position estimation and the frequency difference model are used to estimate the target velocity. The simulations show that the proposed location algorithm has a lower root-mean-square error than the classical two-step weighted least squares method and can maintain high accuracy under large observation errors. Moreover, compared with other hybrid location algorithms, the proposed algorithm boasts a fast convergence speed and can effectively reduce the amount of computation.
For the passive location of radiation sources for motion communications in complex environments, the closed-form solution method is sensitive to measurement noise in time–frequency difference models and has a large root-mean-square error of location. To improve the location performance under large observation errors, this study proposes a recursive hybrid TDOA/FDOA location method, which is based on weighted least squares and the genetic algorithm. Firstly, massive time–frequency difference data are observed at known stations, and error models are built. On this basis of the models, multiple sets of time–frequency difference sequences are processed. Secondly, the initial value of the target position is solved by weighted least squares. Given the initial value, the improved genetic algorithm is used to solve and correct the position coordinates through multiple groups of time–frequency difference sequences iteratively and recursively. Finally, position estimation and the frequency difference model are used to estimate the target velocity. The simulations show that the proposed location algorithm has a lower root-mean-square error than the classical two-step weighted least squares method and can maintain high accuracy under large observation errors. Moreover, compared with other hybrid location algorithms, the proposed algorithm boasts a fast convergence speed and can effectively reduce the amount of computation.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009129
Abstract:
There is a serious multipath effect when the radar measures the target at a low elevation angle. The complex position makes the multipath echo produce irregular reflection, which results in different degrees of amplitude and phase distortion. In this study, a perturbational multipath model is introduced to solve the mismatch between the classical multipath model and the multipath echo reflection of the complex positions, and a height measurement method of the synthesized vector maximum likelihood (SVML) based on the perturbational model is studied. Perturbation parameters are introduced to characterize multipath echo phenomena of complex positions and are obtained by the perturbational multipath sparse Bayesian learning (PSBL) algorithm. The obtained parameters are applied to the SVML algorithm, which improves the height measurement performance of VHF radars in complex positions.
There is a serious multipath effect when the radar measures the target at a low elevation angle. The complex position makes the multipath echo produce irregular reflection, which results in different degrees of amplitude and phase distortion. In this study, a perturbational multipath model is introduced to solve the mismatch between the classical multipath model and the multipath echo reflection of the complex positions, and a height measurement method of the synthesized vector maximum likelihood (SVML) based on the perturbational model is studied. Perturbation parameters are introduced to characterize multipath echo phenomena of complex positions and are obtained by the perturbational multipath sparse Bayesian learning (PSBL) algorithm. The obtained parameters are applied to the SVML algorithm, which improves the height measurement performance of VHF radars in complex positions.
Available online: April 23, 2023 , DOI: 10.15888/j.cnki.csa.009136
Abstract:
On the basis of the discriminative scale space tracking algorithm, the position correction method and Kalman filtering algorithm are applied to pedestrian tracking in this study. Due to deformation and environmental changes, pedestrians cannot be accurately tracked. To solve the problem, this study makes full use of the advantage of the fhog feature in pedestrian tracking and takes the position calculated by the position filter in the discriminative scale space algorithm as the center. It extracts the fhog feature of pedestrians again and correlates it with the position filter template to correct the pedestrian position. Then, the Kalman filtering algorithm is used to predict and correct the corrected pedestrian position again, and finally, a new position filter template is trained in the twice-corrected position. In this study, the pedestrian data set in OTB-100 is selected to test the performance of the method. The experimental results show that in the original algorithm position, the fhog feature is extracted again for correlation operations to correct the position of pedestrians. At the same time, the Kalman filtering predicts and corrects the corrected position, which can improve the positioning accuracy of pedestrians again.
On the basis of the discriminative scale space tracking algorithm, the position correction method and Kalman filtering algorithm are applied to pedestrian tracking in this study. Due to deformation and environmental changes, pedestrians cannot be accurately tracked. To solve the problem, this study makes full use of the advantage of the fhog feature in pedestrian tracking and takes the position calculated by the position filter in the discriminative scale space algorithm as the center. It extracts the fhog feature of pedestrians again and correlates it with the position filter template to correct the pedestrian position. Then, the Kalman filtering algorithm is used to predict and correct the corrected pedestrian position again, and finally, a new position filter template is trained in the twice-corrected position. In this study, the pedestrian data set in OTB-100 is selected to test the performance of the method. The experimental results show that in the original algorithm position, the fhog feature is extracted again for correlation operations to correct the position of pedestrians. At the same time, the Kalman filtering predicts and corrects the corrected position, which can improve the positioning accuracy of pedestrians again.
Available online: April 20, 2023 , DOI: 10.15888/j.cnki.csa.009140
Abstract:
Nowadays, a large amount of medical domain knowledge on the Internet can be used for medical diagnosis, but traditional search engines cannot make reasonable judgments based on the actual situation of patients and fail to meet the needs of use. Therefore, this study mainly develops a question-answering system based on a knowledge graph. The system is applied to the medical field, which uses crawler technology to obtain a large amount of medical data and stores them in the constructed medical knowledge graph of the Neo4j graph database. At the same time, in order to enable the system to further understand the user’s medical questions, this study proposes methods based on BERT and BERT-BiLSTM-CRF models for identifying intent information and entity information in questions, respectively. Finally, the system uses the intent and entity information to make a query in the knowledge graph and provides users with appropriate answers, thus completing the construction of a medical question-answering system
Nowadays, a large amount of medical domain knowledge on the Internet can be used for medical diagnosis, but traditional search engines cannot make reasonable judgments based on the actual situation of patients and fail to meet the needs of use. Therefore, this study mainly develops a question-answering system based on a knowledge graph. The system is applied to the medical field, which uses crawler technology to obtain a large amount of medical data and stores them in the constructed medical knowledge graph of the Neo4j graph database. At the same time, in order to enable the system to further understand the user’s medical questions, this study proposes methods based on BERT and BERT-BiLSTM-CRF models for identifying intent information and entity information in questions, respectively. Finally, the system uses the intent and entity information to make a query in the knowledge graph and provides users with appropriate answers, thus completing the construction of a medical question-answering system
Available online: April 20, 2023 , DOI: 10.15888/j.cnki.csa.009142
Abstract:
The search ability of the sparrow search algorithm is easy to decline due to insufficient diversity of the initialization population, and the algorithm is easy to fall into local optimal in the late search period. In view of these problems, a multi-strategy fusion sparrow search algorithm (ISSA) is proposed. Specifically, the high-dimensional Sine chaotic mapping is introduced to initialize the population in the algorithm’s initialization stage, so as to improve the quality of the initial population and enhance the diversity of the population. Then, the attenuation factor is introduced in the discoverer stage, and the adaptability of the attenuation factor balances the performance of the early global search and the later local optimization. Finally, the Cauchy mutation and change selection strategy are introduced so that the searching individual can jump out of the local limit to continue the search and enhance the local search ability. Six benchmark test functions are randomly selected, and the experimental results verify that ISSA has been effectively improved compared with the original algorithm in terms of finding the optimal value.
The search ability of the sparrow search algorithm is easy to decline due to insufficient diversity of the initialization population, and the algorithm is easy to fall into local optimal in the late search period. In view of these problems, a multi-strategy fusion sparrow search algorithm (ISSA) is proposed. Specifically, the high-dimensional Sine chaotic mapping is introduced to initialize the population in the algorithm’s initialization stage, so as to improve the quality of the initial population and enhance the diversity of the population. Then, the attenuation factor is introduced in the discoverer stage, and the adaptability of the attenuation factor balances the performance of the early global search and the later local optimization. Finally, the Cauchy mutation and change selection strategy are introduced so that the searching individual can jump out of the local limit to continue the search and enhance the local search ability. Six benchmark test functions are randomly selected, and the experimental results verify that ISSA has been effectively improved compared with the original algorithm in terms of finding the optimal value.
Available online: April 20, 2023 , DOI: 10.15888/j.cnki.csa.009143
Abstract:
In the field of photovoltaic panel defect classification, since traditional defect classification methods and emerging machine learning methods have limitations, which fail to meet the requirements for such classification, more reliable solutions are urgently needed. In recent years, few-shot learning, which can quickly learn from limited data and be generalized to new tasks, has gradually sprung up in various fields, bringing new ideas to the optimization of defect technology. Based on a typical few-shot learning method, the prototypical network method, this study proposes an improved prototypical network-based defect classification method for photovoltaic panels. By complicating the model backbone network, improving the model training mode and adjusting the similarity measurement standard, this method can effectively solve the problems of the poor feature embedding ability and general classification effect of the prototypical network for complex samples. The method has been verified by several comparative experiments on a classic photovoltaic panel defect data set. The results show that the experimental time of the improved method is greatly shortened and the model accuracy is improved.
In the field of photovoltaic panel defect classification, since traditional defect classification methods and emerging machine learning methods have limitations, which fail to meet the requirements for such classification, more reliable solutions are urgently needed. In recent years, few-shot learning, which can quickly learn from limited data and be generalized to new tasks, has gradually sprung up in various fields, bringing new ideas to the optimization of defect technology. Based on a typical few-shot learning method, the prototypical network method, this study proposes an improved prototypical network-based defect classification method for photovoltaic panels. By complicating the model backbone network, improving the model training mode and adjusting the similarity measurement standard, this method can effectively solve the problems of the poor feature embedding ability and general classification effect of the prototypical network for complex samples. The method has been verified by several comparative experiments on a classic photovoltaic panel defect data set. The results show that the experimental time of the improved method is greatly shortened and the model accuracy is improved.
Available online: April 20, 2023 , DOI: 10.15888/j.cnki.csa.009162
Abstract:
For limited equipment resources in industrial scenarios, a lightweight strip steel defect detection model based on improved YOLOv5 is proposed. First, ShuffleNetv2 is used to replace the backbone feature extraction network to optimize model parameter amount and running speed; secondly, the lightweight up-sampling operator, namely content-aware reassembly of features (CARAFE) is used to further reduce parameters and calculation amount while increasing the receptive field. At the same time, the GSConv layer is introduced to balance the model accuracy and detection speed while ensuring semantic information. Finally, a cross-level feature fusion mechanism is designed to improve the detection accuracy of the network. The experimental results show that the mean average precision of the improved model is 78.5%, which is 1.4% higher than the original YOLOv5 algorithm. The calculation amount of the model is 10.9 GFLOPs; the parameter amount is 5.88×106; the calculation and parameter amounts are reduced by 31% and 15.4%, respectively; the detection speed is 49 f/s, which is increased by 3.5 f/s. Therefore, the improved model improves the detection accuracy and speed and greatly reduces the calculation and parameter amounts of the model, which can ensure the real-time detection of surface defects of strip steel.
For limited equipment resources in industrial scenarios, a lightweight strip steel defect detection model based on improved YOLOv5 is proposed. First, ShuffleNetv2 is used to replace the backbone feature extraction network to optimize model parameter amount and running speed; secondly, the lightweight up-sampling operator, namely content-aware reassembly of features (CARAFE) is used to further reduce parameters and calculation amount while increasing the receptive field. At the same time, the GSConv layer is introduced to balance the model accuracy and detection speed while ensuring semantic information. Finally, a cross-level feature fusion mechanism is designed to improve the detection accuracy of the network. The experimental results show that the mean average precision of the improved model is 78.5%, which is 1.4% higher than the original YOLOv5 algorithm. The calculation amount of the model is 10.9 GFLOPs; the parameter amount is 5.88×106; the calculation and parameter amounts are reduced by 31% and 15.4%, respectively; the detection speed is 49 f/s, which is increased by 3.5 f/s. Therefore, the improved model improves the detection accuracy and speed and greatly reduces the calculation and parameter amounts of the model, which can ensure the real-time detection of surface defects of strip steel.
Available online: April 20, 2023 , DOI: 10.15888/j.cnki.csa.009153
Abstract:
The registration technology of medical three-dimensional (3D) images (such as CT, MRI, etc.) and two-dimensional (2D) images (such as X-ray) has been widely used in clinical diagnosis and surgical planning. The essence of medical image registration is to use an optimization algorithm to find some kind of spatial transformation so that two images are aligned in space and structure. Usually, the registration quality is low in the process of registration due to the problem that the optimization algorithm is not accurate and easy to fall into the local extremum. In order to solve this problem, an improved equilibrium optimizer based on the Logistic-Tent chaos map and Levy flight (LTEO) is proposed. First, in order to solve the problem that the population initialization is easy to be unevenly distributed, and the randomness is too high, the Logistic-Tent chaotic map is introduced to initialize the population, increase the diversity of the population, and make them distribute in the search space as much as possible; second, the iterative function is updated to make the optimization algorithm pay more attention to the global search, improve the convergence speed of the algorithm, and help to find the global optimum solution; third, Levy flight strategy is introduced to disturb the stagnant particles and thus prevent the algorithm from falling into local extremum. Finally, LTEO is used for 2D/3D medical image registration tasks, and the frequent transmission of data in the registration process is optimized to reduce the time consumption of registration. The algorithm is verified by benchmark function tests and clinical registration experiments. The LTEO can effectively improve optimization accuracy and stability and enhance the quality of medical image registration.
The registration technology of medical three-dimensional (3D) images (such as CT, MRI, etc.) and two-dimensional (2D) images (such as X-ray) has been widely used in clinical diagnosis and surgical planning. The essence of medical image registration is to use an optimization algorithm to find some kind of spatial transformation so that two images are aligned in space and structure. Usually, the registration quality is low in the process of registration due to the problem that the optimization algorithm is not accurate and easy to fall into the local extremum. In order to solve this problem, an improved equilibrium optimizer based on the Logistic-Tent chaos map and Levy flight (LTEO) is proposed. First, in order to solve the problem that the population initialization is easy to be unevenly distributed, and the randomness is too high, the Logistic-Tent chaotic map is introduced to initialize the population, increase the diversity of the population, and make them distribute in the search space as much as possible; second, the iterative function is updated to make the optimization algorithm pay more attention to the global search, improve the convergence speed of the algorithm, and help to find the global optimum solution; third, Levy flight strategy is introduced to disturb the stagnant particles and thus prevent the algorithm from falling into local extremum. Finally, LTEO is used for 2D/3D medical image registration tasks, and the frequent transmission of data in the registration process is optimized to reduce the time consumption of registration. The algorithm is verified by benchmark function tests and clinical registration experiments. The LTEO can effectively improve optimization accuracy and stability and enhance the quality of medical image registration.
Available online: April 20, 2023 , DOI: 10.15888/j.cnki.csa.009156
Abstract:
Ramps are crucial to offshore platforms, and their absence will cause great safety risks to operation sites. To eliminate such risks, this study proposes a detection method of ramp setting up under the berthing row scenario. The method is divided into three parts: firstly, using the object detection algorithm to locate and mark the target; then, extracting the external edge of the marked target area by edge detection; finally, formulating the specific safety measures discrimination algorithm to identify violations in the work site. To solve the detection problems of small targets, this method improves the YOLOv5 and introduces an attention module in feature extraction and feature fusion, which makes the model more lightweight while improving its mean average precision (mAP) from 53.1% to 54.5%. As to rough edge detection, the loss function of the edge detection network PiDiNet is improved. Compared with the original network, the false detection rate decreases from 8.9% to 5.4%. The verification results indicate that the method can be used to detect whether the ramp is set up correctly within the effective time with accuracy up to 91.5%.
Ramps are crucial to offshore platforms, and their absence will cause great safety risks to operation sites. To eliminate such risks, this study proposes a detection method of ramp setting up under the berthing row scenario. The method is divided into three parts: firstly, using the object detection algorithm to locate and mark the target; then, extracting the external edge of the marked target area by edge detection; finally, formulating the specific safety measures discrimination algorithm to identify violations in the work site. To solve the detection problems of small targets, this method improves the YOLOv5 and introduces an attention module in feature extraction and feature fusion, which makes the model more lightweight while improving its mean average precision (mAP) from 53.1% to 54.5%. As to rough edge detection, the loss function of the edge detection network PiDiNet is improved. Compared with the original network, the false detection rate decreases from 8.9% to 5.4%. The verification results indicate that the method can be used to detect whether the ramp is set up correctly within the effective time with accuracy up to 91.5%.
Available online: April 20, 2023 , DOI: 10.15888/j.cnki.csa.009138
Abstract:
To tackle the problem that the existing face image translation models cannot realize the translation among multiple visual attributes and the translated face images are not clear and natural, this study proposes a multi-attribute face image translation model based on the face recognition method. The model is mainly composed of the content and style encoder, AdaIN decoder, and face recognition module. First, the two encoders extract the potential encoding of the content and style image and then send the encoding into the AdaIN layer for affine transformation, and finally the decoder restores the translated image. A face recognition model is designed and trained using this method with an accuracy rate of 90.282%. A joint face attribute loss function is proposed, which enhances the model’s attention to the attributes of the style face, solves the problem that the model cannot accurately extract the attribute information of the face, and discards irrelevant information so that the model can generate clear, multi-attribute, and diverse face translation images. This method is tested on the open dataset CelebA-HQ, whose results are higher than the baselines in terms of both quantitative and qualitative indicators. It also shows good robustness in different face orientations. The image generated by the model can also be used in the field of face image generation to address dataset shortage.
To tackle the problem that the existing face image translation models cannot realize the translation among multiple visual attributes and the translated face images are not clear and natural, this study proposes a multi-attribute face image translation model based on the face recognition method. The model is mainly composed of the content and style encoder, AdaIN decoder, and face recognition module. First, the two encoders extract the potential encoding of the content and style image and then send the encoding into the AdaIN layer for affine transformation, and finally the decoder restores the translated image. A face recognition model is designed and trained using this method with an accuracy rate of 90.282%. A joint face attribute loss function is proposed, which enhances the model’s attention to the attributes of the style face, solves the problem that the model cannot accurately extract the attribute information of the face, and discards irrelevant information so that the model can generate clear, multi-attribute, and diverse face translation images. This method is tested on the open dataset CelebA-HQ, whose results are higher than the baselines in terms of both quantitative and qualitative indicators. It also shows good robustness in different face orientations. The image generated by the model can also be used in the field of face image generation to address dataset shortage.
Available online: April 17, 2023 , DOI: 10.15888/j.cnki.csa.008936
Abstract:
Education is an important enabler for achieving sustainable development goals (SDGs). Artificial intelligence (AI) is a booming technology, and people are showing increasing interests in understanding students’ behavior and evaluating their performance. For the SDGs, AI has great potential to improve education as it has started to be developed in the education field with innovative teaching methods to create better learning. This study presents an artificial intelligence-based analytic tool for predicting the performance of students in a first-year information technology course at a university. A random forest-based classification model is built to predict students’ performance in Week 6, and the model reports the accuracy of 97.03%, sensitivity of 95.26%, specificity of 98.8%, precision of 98.86%, and the Mathews correlation coefficient of 94%. The result demonstrates that this method is useful in predicting the early performance of students in courses. During the COVID-19 pandemic, experimental results showed that the proposed prediction model met the accuracy, precision, and recall required to predict elements of students’ learning behavior in a virtual education system.
Education is an important enabler for achieving sustainable development goals (SDGs). Artificial intelligence (AI) is a booming technology, and people are showing increasing interests in understanding students’ behavior and evaluating their performance. For the SDGs, AI has great potential to improve education as it has started to be developed in the education field with innovative teaching methods to create better learning. This study presents an artificial intelligence-based analytic tool for predicting the performance of students in a first-year information technology course at a university. A random forest-based classification model is built to predict students’ performance in Week 6, and the model reports the accuracy of 97.03%, sensitivity of 95.26%, specificity of 98.8%, precision of 98.86%, and the Mathews correlation coefficient of 94%. The result demonstrates that this method is useful in predicting the early performance of students in courses. During the COVID-19 pandemic, experimental results showed that the proposed prediction model met the accuracy, precision, and recall required to predict elements of students’ learning behavior in a virtual education system.
Available online: April 17, 2023 , DOI: 10.15888/j.cnki.csa.009108
Abstract:
The classical artificial bee colony (ABC) algorithm is also faced with slow convergence speed, and it is easy to fall into local optimality, so there are still many problems in feature selection based on this algorithm. Therefore, a feature selection method based on the rough entropy of granularity and an improved bee colony algorithm, namely FS_GREIABC, is proposed. Firstly, a new information entropy model, namely the rough entropy of granularity, is proposed by combining the knowledge granularity and the rough entropy in the rough set. Secondly, the rough entropy of granularity is applied to the ABC algorithm, and a fitness function based on the rough entropy of granularity is proposed, so as to obtain a new fitness calculation strategy. Thirdly, in order to improve the local search ability of the ABC algorithm, a cloud model is introduced into the following bee stage. Experiments on multiple UCI datasets and software defect prediction datasets show that FS_GREIABC not only selects fewer features but also has better classification performance than the existing feature selection algorithms.
The classical artificial bee colony (ABC) algorithm is also faced with slow convergence speed, and it is easy to fall into local optimality, so there are still many problems in feature selection based on this algorithm. Therefore, a feature selection method based on the rough entropy of granularity and an improved bee colony algorithm, namely FS_GREIABC, is proposed. Firstly, a new information entropy model, namely the rough entropy of granularity, is proposed by combining the knowledge granularity and the rough entropy in the rough set. Secondly, the rough entropy of granularity is applied to the ABC algorithm, and a fitness function based on the rough entropy of granularity is proposed, so as to obtain a new fitness calculation strategy. Thirdly, in order to improve the local search ability of the ABC algorithm, a cloud model is introduced into the following bee stage. Experiments on multiple UCI datasets and software defect prediction datasets show that FS_GREIABC not only selects fewer features but also has better classification performance than the existing feature selection algorithms.
Available online: April 17, 2023 , DOI: 10.15888/j.cnki.csa.009157
Abstract:
In the current research on multi-intention recognition models of natural language processing, information flow is only modeled from intention to slot, and the research on the interactive modeling of information flow from slot to intention is ignored. In addition, the task of intention recognition is easy to be confused, and other intention information is wrongly captured. The quality of contextual semantic feature extraction is poor and needs to be improved. In order to solve these problems, this study optimizes the current advanced typical GL-GIN (global-locally graph interaction network) model, explores the interactive modeling method from slot to intention, and uses the one-way attention layer from slot to intention. Furthermore, the study calculates the attention score from slot to intention, incorporates the attention mechanism, and uses the attention score from slot to intention as the connection weight. As a result, it can propagate and gather intention-related slot information and make the intention focus on the slot information that is relevant to it, so as to realize the bidirectional information flow of the multi-intention recognition model. At the same time, the BERT model is introduced as the coding layer to improve the quality of semantic feature extraction. Experiments show that the effect of this interactive modeling method is significantly improved. Compared with that of the original GL-GIN model, the overall accuracy of the new model on two public datasets (MixATIS and MixSNIPS) is increased by 5.2% and 9%, respectively.
In the current research on multi-intention recognition models of natural language processing, information flow is only modeled from intention to slot, and the research on the interactive modeling of information flow from slot to intention is ignored. In addition, the task of intention recognition is easy to be confused, and other intention information is wrongly captured. The quality of contextual semantic feature extraction is poor and needs to be improved. In order to solve these problems, this study optimizes the current advanced typical GL-GIN (global-locally graph interaction network) model, explores the interactive modeling method from slot to intention, and uses the one-way attention layer from slot to intention. Furthermore, the study calculates the attention score from slot to intention, incorporates the attention mechanism, and uses the attention score from slot to intention as the connection weight. As a result, it can propagate and gather intention-related slot information and make the intention focus on the slot information that is relevant to it, so as to realize the bidirectional information flow of the multi-intention recognition model. At the same time, the BERT model is introduced as the coding layer to improve the quality of semantic feature extraction. Experiments show that the effect of this interactive modeling method is significantly improved. Compared with that of the original GL-GIN model, the overall accuracy of the new model on two public datasets (MixATIS and MixSNIPS) is increased by 5.2% and 9%, respectively.
Available online: April 14, 2023 , DOI: 10.15888/j.cnki.csa.009133
Abstract:
Anomaly detection system plays a significant role in cyberspace security and provides effective protection for network security. Regarding complex network traffic information, the traditional single classifier is often unable to ensure high detection accuracy and strong generalization ability at the same time. In addition, the anomaly detection model based on full features is often disturbed by redundancy features, which affects the accuracy and efficiency of detection. To address these problems, this study proposes a feature selection and ensemble learning model based on average feature importance. The decision tree (DT), random forest (RF), and extra tree (ET) are selected as the base classifiers to establish a voting ensemble model, and the average feature importance of the base classifiers is calculated based on the Gini coefficient for feature selection. The experimental evaluation results on several datasets show that the proposed model is superior to the classical ensemble learning models and other well-known anomaly detection ensemble models. The proposed model can improve the accuracy of the ensemble model by about 0.13% and save about 30% of training time on average.
Anomaly detection system plays a significant role in cyberspace security and provides effective protection for network security. Regarding complex network traffic information, the traditional single classifier is often unable to ensure high detection accuracy and strong generalization ability at the same time. In addition, the anomaly detection model based on full features is often disturbed by redundancy features, which affects the accuracy and efficiency of detection. To address these problems, this study proposes a feature selection and ensemble learning model based on average feature importance. The decision tree (DT), random forest (RF), and extra tree (ET) are selected as the base classifiers to establish a voting ensemble model, and the average feature importance of the base classifiers is calculated based on the Gini coefficient for feature selection. The experimental evaluation results on several datasets show that the proposed model is superior to the classical ensemble learning models and other well-known anomaly detection ensemble models. The proposed model can improve the accuracy of the ensemble model by about 0.13% and save about 30% of training time on average.
Available online: April 14, 2023 , DOI: 10.15888/j.cnki.csa.009126
Abstract:
Domain adaptation is a transfer learning algorithm used when the training and test sets do not satisfy the independent homogeneous distribution condition. When the distribution difference between two domains is large, the intra-domain transferability will be reduced, and the existing domain adaptation algorithms need to obtain a large amount of target domain data, which cannot be achieved in some practical applications. In view of the shortcomings of existing domain adaptation methods, the convolutional neural network model is used, and a domain adaptation algorithm based on feature center alignment for few-shot learning is proposed to find domain invariant features, improve the distinguishability of target domain features, and strengthen the classification accuracy. Simulation and experimental results for office-31 public dataset recognition and radar working pattern recognition under small sample conditions show that the proposed method improves the average recognition accuracy of the office-31 dataset by 12.9% compared with the maximum mean discrepancy method, and the radar working pattern recognition accuracy reaches 91%, which is 10% better than the maximum mean discrepancy method.
Domain adaptation is a transfer learning algorithm used when the training and test sets do not satisfy the independent homogeneous distribution condition. When the distribution difference between two domains is large, the intra-domain transferability will be reduced, and the existing domain adaptation algorithms need to obtain a large amount of target domain data, which cannot be achieved in some practical applications. In view of the shortcomings of existing domain adaptation methods, the convolutional neural network model is used, and a domain adaptation algorithm based on feature center alignment for few-shot learning is proposed to find domain invariant features, improve the distinguishability of target domain features, and strengthen the classification accuracy. Simulation and experimental results for office-31 public dataset recognition and radar working pattern recognition under small sample conditions show that the proposed method improves the average recognition accuracy of the office-31 dataset by 12.9% compared with the maximum mean discrepancy method, and the radar working pattern recognition accuracy reaches 91%, which is 10% better than the maximum mean discrepancy method.
Available online: April 14, 2023 , DOI: 10.15888/j.cnki.csa.009127
Abstract:
In construction sites, many high fall accidents have occurred, so it is necessary to wear helmets. An improved algorithm based on YOLOX-s is proposed to deal with missing and omitted detection of small target samples encountered in helmet-wearing condition detection. First, the 160×160 feature layer in the Neck layer is introduced in the backbone feature extraction network for feature fusion, and a detection head for small targets is added; second, the SIoU loss function is used to calculate the loss value, which makes the loss term considered in the training process of the network more comprehensive, and the varifocal loss function is used to calculate the loss value of the confidence level to further reduce the imbalance of the positive and difficult samples in the training process; finally, coordinate attention (CA) mechanism is used to enhance the feature representation of the model. The experimental results show that the optimization of the Neck layer, detection layer, and loss function and the introduction of the CA mechanism lead to better convergence and regression performance of the network during the training process. The mAP value of the improved algorithm is 95.57%, which is 17.11% and 3.59% higher than that of YOLOv3 and the original YOLOX-s algorithm, respectively. The detection speed of the improved algorithm is 54.73 frames/s, which meets the real-time detection speed requirement.
In construction sites, many high fall accidents have occurred, so it is necessary to wear helmets. An improved algorithm based on YOLOX-s is proposed to deal with missing and omitted detection of small target samples encountered in helmet-wearing condition detection. First, the 160×160 feature layer in the Neck layer is introduced in the backbone feature extraction network for feature fusion, and a detection head for small targets is added; second, the SIoU loss function is used to calculate the loss value, which makes the loss term considered in the training process of the network more comprehensive, and the varifocal loss function is used to calculate the loss value of the confidence level to further reduce the imbalance of the positive and difficult samples in the training process; finally, coordinate attention (CA) mechanism is used to enhance the feature representation of the model. The experimental results show that the optimization of the Neck layer, detection layer, and loss function and the introduction of the CA mechanism lead to better convergence and regression performance of the network during the training process. The mAP value of the improved algorithm is 95.57%, which is 17.11% and 3.59% higher than that of YOLOv3 and the original YOLOX-s algorithm, respectively. The detection speed of the improved algorithm is 54.73 frames/s, which meets the real-time detection speed requirement.
Available online: April 14, 2023 , DOI: 10.15888/j.cnki.csa.009122
Abstract:
The user persona is a sketch and description of the user image, which has been widely used in typical retail scenarios such as the wake-up of sleeping members, prediction of users arriving at the store, and personalized recommendations. Drugs are different from ordinary commodities, and they contain strong semantic knowledge. The existing user persona mainly starts from the consumption attribute and static attribute and is not completely applicable to the pharmacy marketing and prediction field. This study proposes a persona of pharmacy user (UPP) model for the drug field, which embeds medical knowledge on the existing persona and uses methods such as rules, clustering, statistics, and entity recognition to extract new labels including chronic diseases, diseases, special diseases, activity sensitivity, user value, and price preference. All labels are integrated into a clustering-based group division method to form the user profile. The experiment shows that the accuracy of this model is 13% higher than the existing user persona model in the consumer behavior prediction scenario, so the proposed model is more suitable for the pharmacy marketing scenario.
The user persona is a sketch and description of the user image, which has been widely used in typical retail scenarios such as the wake-up of sleeping members, prediction of users arriving at the store, and personalized recommendations. Drugs are different from ordinary commodities, and they contain strong semantic knowledge. The existing user persona mainly starts from the consumption attribute and static attribute and is not completely applicable to the pharmacy marketing and prediction field. This study proposes a persona of pharmacy user (UPP) model for the drug field, which embeds medical knowledge on the existing persona and uses methods such as rules, clustering, statistics, and entity recognition to extract new labels including chronic diseases, diseases, special diseases, activity sensitivity, user value, and price preference. All labels are integrated into a clustering-based group division method to form the user profile. The experiment shows that the accuracy of this model is 13% higher than the existing user persona model in the consumer behavior prediction scenario, so the proposed model is more suitable for the pharmacy marketing scenario.
Available online: April 14, 2023 , DOI: 10.15888/j.cnki.csa.009116
Abstract:
Most aspect level sentiment analysis methods do not focus on keyword features in the local context. Therefore, this study proposes an aspect level sentiment analysis model LCPM (local context pos mask) based on local context keyword feature extraction and enhancement. First, a local context part of the speech mask mechanism is proposed to extract the important words features around aspect words and reduce the interference of noise words. Second, the loss function is modified, so that the model focuses on the local context keyword features related to aspect words and improves the performance of the model’s sentimental classification. Finally, a gating mechanism is designed. The model can dynamically learn the weight coefficients and assign different weight coefficients to local context keyword features and global context features. The experiments on four open datasets show that, compared with existing aspect level sentiment analysis models, the proposed model has higher accuracy and MF1 value, which verifies the effectiveness of local context keyword extraction and enhancement and is of application significance in aspect level sentiment analysis tasks.
Most aspect level sentiment analysis methods do not focus on keyword features in the local context. Therefore, this study proposes an aspect level sentiment analysis model LCPM (local context pos mask) based on local context keyword feature extraction and enhancement. First, a local context part of the speech mask mechanism is proposed to extract the important words features around aspect words and reduce the interference of noise words. Second, the loss function is modified, so that the model focuses on the local context keyword features related to aspect words and improves the performance of the model’s sentimental classification. Finally, a gating mechanism is designed. The model can dynamically learn the weight coefficients and assign different weight coefficients to local context keyword features and global context features. The experiments on four open datasets show that, compared with existing aspect level sentiment analysis models, the proposed model has higher accuracy and MF1 value, which verifies the effectiveness of local context keyword extraction and enhancement and is of application significance in aspect level sentiment analysis tasks.
Available online: April 14, 2023 , DOI: 10.15888/j.cnki.csa.009117
Abstract:
Anchor-free-based detection methods have been proposed successively in recent years, and they transform objects into key points and assign labels to positive and negative samples in the global Gaussian heatmap. This label assignment strategy suffers from positive and negative sample imbalance in some scenarios and cannot effectively reflect the shape and orientation of the object in parathyroid detection. Therefore, a new parathyroid detection model, namely, EllipseNet, is proposed in this study, which first constructs an elliptical Gaussian distribution in GT to fit the real object in GT, so as to make the assignment of positive and negative samples more fine-grained. Furthermore, a loss function incorporating the object shape information is proposed to constrain the position of the object, so as to improve the accuracy of detection. In addition, multi-scale prediction is constructed in the model, which can better detect objects of different sizes and solve the problem of target scale imbalance in parathyroid detection. In this study, experiments are conducted on the parathyroid dataset, and the results show that EllipseNet achieves an AP50 of 95%, which is a large improvement in detection accuracy compared with a variety of mainstream detection algorithms.
Anchor-free-based detection methods have been proposed successively in recent years, and they transform objects into key points and assign labels to positive and negative samples in the global Gaussian heatmap. This label assignment strategy suffers from positive and negative sample imbalance in some scenarios and cannot effectively reflect the shape and orientation of the object in parathyroid detection. Therefore, a new parathyroid detection model, namely, EllipseNet, is proposed in this study, which first constructs an elliptical Gaussian distribution in GT to fit the real object in GT, so as to make the assignment of positive and negative samples more fine-grained. Furthermore, a loss function incorporating the object shape information is proposed to constrain the position of the object, so as to improve the accuracy of detection. In addition, multi-scale prediction is constructed in the model, which can better detect objects of different sizes and solve the problem of target scale imbalance in parathyroid detection. In this study, experiments are conducted on the parathyroid dataset, and the results show that EllipseNet achieves an AP50 of 95%, which is a large improvement in detection accuracy compared with a variety of mainstream detection algorithms.
Available online: March 24, 2023 , DOI: 10.15888/j.cnki.csa.009091
Abstract:
Heavy pollution weather is the key target of air pollution control during the “Fourteenth Five-Year Plan” period. Accurate identification of risk sources during the period of heavy pollution weather paves the way for early warning in time, effective environmental pollution control, and prevention of the further aggravation of pollution incidents. On the basis of the data obtained by grid monitoring technology, this study proposes a deep learning model combining the residual network (ResNet), graph convolutional network (GCN), and gated recurrent unit (GRU) network, i.e., the ResGCN-GRU. This model is mainly used to identify risk sources during the period of heavy pollution weather. The risk sources of such weather are often regional and have salient spatiotemporal features. Therefore, this study starts by extracting the spatial features among monitoring points with the GCN and solving the problems of over-smoothing and gradient disappearance caused by the multi-layer GCN with the ResNet. Then, the GRU is used to extract the temporal features of risk sources. Finally, the spatiotemporal features fused by the fully connected layer are input into the Softmax activation function to obtain the binary classification probability, which is further used to obtain the classification result. To verify the performance of the proposed model, this study analyzes the data of 72 monitoring points in Shenyang and compares GCN, long short-term memory (LSTM), GRU, and GCN-GRU in accuracy, recall rate, and comprehensive evaluation indicators. The experimental results show that the classification accuracy of the ResGCN-GRU model is 16.9%, 4.3%, 3.1%, and 2.9% higher than that of the above four models, respectively, which proves that the model proposed in this study is more effective in identifying air risk sources, and it can accurately identify risk sources according to the spatiotemporal features of risk source data.
Heavy pollution weather is the key target of air pollution control during the “Fourteenth Five-Year Plan” period. Accurate identification of risk sources during the period of heavy pollution weather paves the way for early warning in time, effective environmental pollution control, and prevention of the further aggravation of pollution incidents. On the basis of the data obtained by grid monitoring technology, this study proposes a deep learning model combining the residual network (ResNet), graph convolutional network (GCN), and gated recurrent unit (GRU) network, i.e., the ResGCN-GRU. This model is mainly used to identify risk sources during the period of heavy pollution weather. The risk sources of such weather are often regional and have salient spatiotemporal features. Therefore, this study starts by extracting the spatial features among monitoring points with the GCN and solving the problems of over-smoothing and gradient disappearance caused by the multi-layer GCN with the ResNet. Then, the GRU is used to extract the temporal features of risk sources. Finally, the spatiotemporal features fused by the fully connected layer are input into the Softmax activation function to obtain the binary classification probability, which is further used to obtain the classification result. To verify the performance of the proposed model, this study analyzes the data of 72 monitoring points in Shenyang and compares GCN, long short-term memory (LSTM), GRU, and GCN-GRU in accuracy, recall rate, and comprehensive evaluation indicators. The experimental results show that the classification accuracy of the ResGCN-GRU model is 16.9%, 4.3%, 3.1%, and 2.9% higher than that of the above four models, respectively, which proves that the model proposed in this study is more effective in identifying air risk sources, and it can accurately identify risk sources according to the spatiotemporal features of risk source data.
Available online: March 24, 2023 , DOI: 10.15888/j.cnki.csa.009118
Abstract:
For processing graph data, a variety of graph neural network approaches have been developed; however, most research focuses on the convolutional layer for feature aggregation rather than the pooling layer for downsampling. Additionally, the computation of assignment matrices is required for the pooling approach to creating clusters, and the pooling method for node scores simply employs one scoring strategy. A new multi-dimension graph pooling operator, MDPool, is presented to solve these issues and increase the precision of graph classification tasks. To calculate node scores in various dimensions, the model makes use of information on node features and graph structure. The score weighting across several dimensions is summarized by using an attention technique to provide more reliable node rankings. The set of nodes is chosen to produce induced subgraphs based on the node rankings. The proposed MDPool can be implemented into a variety of graph neural network architectures. The encode-decode model, EDMDPool, is created by stacking the MDPool pooling operator with the convolutional layer of the graph neural network. In the graph classification tasks of four public datasets, EDMDPool performs better than the existing baseline model.
For processing graph data, a variety of graph neural network approaches have been developed; however, most research focuses on the convolutional layer for feature aggregation rather than the pooling layer for downsampling. Additionally, the computation of assignment matrices is required for the pooling approach to creating clusters, and the pooling method for node scores simply employs one scoring strategy. A new multi-dimension graph pooling operator, MDPool, is presented to solve these issues and increase the precision of graph classification tasks. To calculate node scores in various dimensions, the model makes use of information on node features and graph structure. The score weighting across several dimensions is summarized by using an attention technique to provide more reliable node rankings. The set of nodes is chosen to produce induced subgraphs based on the node rankings. The proposed MDPool can be implemented into a variety of graph neural network architectures. The encode-decode model, EDMDPool, is created by stacking the MDPool pooling operator with the convolutional layer of the graph neural network. In the graph classification tasks of four public datasets, EDMDPool performs better than the existing baseline model.
Available online: March 24, 2023 , DOI: 10.15888/j.cnki.csa.009114
Abstract:
To address the problem that incremental intrusion detection algorithms do not classify old category data with high accuracy due to catastrophic forgetting of old knowledge, this study proposes an incremental intrusion detection algorithm (ImFace) based on asymmetric multi-feature fusion auto-encoder (AMAE) and fully connected classification deep neural network (C-DNN). In the incremental learning phase, ImFace trains an AMAE model and a C-DNN model for each new batch of the dataset. At the same time, this paper solves the problem of C-DNN’s insufficient ability to detect certain categories of data due to unbalanced datasets by oversampling the data through a variational auto-encoder (VAE). In the detection phase, ImFace makes the input data pass through all AMAEs and C-DNNs and then uses the result of AMAEs as the confidence level to select the output result of a C-DNN as the final result. In this study, the CICIDS2017 dataset is used to test the effectiveness of the ImFace algorithm. The experimental results show that the ImFace algorithm not only retains the ability to classify old categories but also has a high detection accuracy for new categories of data.
To address the problem that incremental intrusion detection algorithms do not classify old category data with high accuracy due to catastrophic forgetting of old knowledge, this study proposes an incremental intrusion detection algorithm (ImFace) based on asymmetric multi-feature fusion auto-encoder (AMAE) and fully connected classification deep neural network (C-DNN). In the incremental learning phase, ImFace trains an AMAE model and a C-DNN model for each new batch of the dataset. At the same time, this paper solves the problem of C-DNN’s insufficient ability to detect certain categories of data due to unbalanced datasets by oversampling the data through a variational auto-encoder (VAE). In the detection phase, ImFace makes the input data pass through all AMAEs and C-DNNs and then uses the result of AMAEs as the confidence level to select the output result of a C-DNN as the final result. In this study, the CICIDS2017 dataset is used to test the effectiveness of the ImFace algorithm. The experimental results show that the ImFace algorithm not only retains the ability to classify old categories but also has a high detection accuracy for new categories of data.
Available online: March 17, 2023 , DOI: 10.15888/j.cnki.csa.009115
Abstract:
As weather radar technology develops, traditional monolithic radar product generation systems are unable to meet the requirements of rapid detection rate and multi-source data in new weather radar systems. In this study, a distributed radar product generation system based on message scheduling is proposed to improve the real-time and scalability of the system. Based on ActiveMQ message middleware, a task scheduling strategy of the distributed cluster is designed. The Ceph file system is employed to build a unified and efficient storage service, and the parallel acceleration of the meteorological algorithm is implemented by GPU. At present, this system has been delivered and applied in actual projects and has yielded good results. Therefore, it is of certain promotion value and application significance.
As weather radar technology develops, traditional monolithic radar product generation systems are unable to meet the requirements of rapid detection rate and multi-source data in new weather radar systems. In this study, a distributed radar product generation system based on message scheduling is proposed to improve the real-time and scalability of the system. Based on ActiveMQ message middleware, a task scheduling strategy of the distributed cluster is designed. The Ceph file system is employed to build a unified and efficient storage service, and the parallel acceleration of the meteorological algorithm is implemented by GPU. At present, this system has been delivered and applied in actual projects and has yielded good results. Therefore, it is of certain promotion value and application significance.
Available online: March 17, 2023 , DOI: 10.15888/j.cnki.csa.009113
Abstract:
In order to address the problem of low accuracy of skin melanoma lesion segmentation in existing image segmentation methods, a MultiResUNet-SMIS method is proposed based on existing convolution neural network methods. Firstly, according to the imaging characteristics of skin melanoma, the dilation convolution with different dilation rates is introduced to replace the normal convolution, and the receptive field is expanded on the premise of the same parameters so that the model can segment the lesion at multiple scales. Secondly, spatial and channel attention mechanisms are added to the model to redistribute feature weights, expand the influence of features of interest, and suppress irrelevant features. Finally, by combining Focal loss with Dice loss, a new loss function, i.e., FD loss, is proposed to calculate the regression loss and solve the problem of unbalanced foreground and background pixels, so as to further improve the segmentation accuracy of the network model. The experimental results show that Dice, IoU, and Acc of MultiResUNet-SMIS on the ISIC-2018 dataset have reached 89.47%, 82.67%, and 96.13%, respectively, which are better than the original MultiResUNet and mainstream methods such as UNet, UNet++, and DeepLab V3+ in skin melanoma image segmentation.
In order to address the problem of low accuracy of skin melanoma lesion segmentation in existing image segmentation methods, a MultiResUNet-SMIS method is proposed based on existing convolution neural network methods. Firstly, according to the imaging characteristics of skin melanoma, the dilation convolution with different dilation rates is introduced to replace the normal convolution, and the receptive field is expanded on the premise of the same parameters so that the model can segment the lesion at multiple scales. Secondly, spatial and channel attention mechanisms are added to the model to redistribute feature weights, expand the influence of features of interest, and suppress irrelevant features. Finally, by combining Focal loss with Dice loss, a new loss function, i.e., FD loss, is proposed to calculate the regression loss and solve the problem of unbalanced foreground and background pixels, so as to further improve the segmentation accuracy of the network model. The experimental results show that Dice, IoU, and Acc of MultiResUNet-SMIS on the ISIC-2018 dataset have reached 89.47%, 82.67%, and 96.13%, respectively, which are better than the original MultiResUNet and mainstream methods such as UNet, UNet++, and DeepLab V3+ in skin melanoma image segmentation.
Available online: September 01, 2022 , DOI: 10.15888/j.cnki.csa.008808
Abstract:
PM2.5 is an important indicator for measuring the concentration of air pollutants, and monitoring and predicting its concentration can effectively protect the atmospheric environment and further reduce the harm caused by air pollution. As automatic air quality monitoring stations are constructed on a large scale, the air quality prediction model built by traditional machine learning can no longer meet the current needs. This study proposes a Gaussian-attention prediction model based on the multi-head attention mechanism and Gaussian probability estimation and utilizes the data from a monitoring station in Shenyang for training and tests. Because PM2.5 concentration is affected by other air quality data, this model uses the information alignment of hierarchical time stamps (week, day, and hour) of air quality data as input and extracts the time-series correlation features of different subspaces with the multi-head attention mechanism. More complete and effective feature information is thereby obtained, and prediction results are then acquired by Gaussian likelihood estimation. A comparison with multiple benchmark models is conducted, and the mean squared error (MSE) and mean absolute error (MAE) of the proposed Gaussian-attention prediction model are respectively 21% and 15% lower than that of the DeepAR model. Effectively improving prediction accuracy, the proposed model can accurately predict PM2.5 concentration.
PM2.5 is an important indicator for measuring the concentration of air pollutants, and monitoring and predicting its concentration can effectively protect the atmospheric environment and further reduce the harm caused by air pollution. As automatic air quality monitoring stations are constructed on a large scale, the air quality prediction model built by traditional machine learning can no longer meet the current needs. This study proposes a Gaussian-attention prediction model based on the multi-head attention mechanism and Gaussian probability estimation and utilizes the data from a monitoring station in Shenyang for training and tests. Because PM2.5 concentration is affected by other air quality data, this model uses the information alignment of hierarchical time stamps (week, day, and hour) of air quality data as input and extracts the time-series correlation features of different subspaces with the multi-head attention mechanism. More complete and effective feature information is thereby obtained, and prediction results are then acquired by Gaussian likelihood estimation. A comparison with multiple benchmark models is conducted, and the mean squared error (MSE) and mean absolute error (MAE) of the proposed Gaussian-attention prediction model are respectively 21% and 15% lower than that of the DeepAR model. Effectively improving prediction accuracy, the proposed model can accurately predict PM2.5 concentration.
Available online: March 31, 2022 , DOI: 10.15888/j.cnki.csa.008603
Abstract:
The security of electric energy plays an important role in national security. With the development of power 5G communication, a large number of power terminals have positioning demand. The traditional global positioning system (GPS) is vulnerable to spoofing. How to improve the security of GPS effectively has become an urgent problem. This study proposes a GPS spoofing detection algorithm with base station assistance in power 5G terminals. It uses the base station positioning with high security to verify the GPS positioning that may be spoofed and introduces the consistency factor (CF) to measure the consistency between GPS positioning and base station positioning. If CF is greater than a threshold, the GPS positioning is classified as spoofed. Otherwise, it is judged as normal. The experimental results show that the accuracy of the algorithm is 99.98%, higher than that of traditional classification algorithms based on machine learning. In addition, our scheme is also faster than those algorithms.
The security of electric energy plays an important role in national security. With the development of power 5G communication, a large number of power terminals have positioning demand. The traditional global positioning system (GPS) is vulnerable to spoofing. How to improve the security of GPS effectively has become an urgent problem. This study proposes a GPS spoofing detection algorithm with base station assistance in power 5G terminals. It uses the base station positioning with high security to verify the GPS positioning that may be spoofed and introduces the consistency factor (CF) to measure the consistency between GPS positioning and base station positioning. If CF is greater than a threshold, the GPS positioning is classified as spoofed. Otherwise, it is judged as normal. The experimental results show that the accuracy of the algorithm is 99.98%, higher than that of traditional classification algorithms based on machine learning. In addition, our scheme is also faster than those algorithms.
-
全文下载排行(总排行，年度排行，各期排行)
摘要点击排行(总排行，年度排行，各期排行)
Article Search
Search by issue
Select AllDeselectExport
Display Method:
Abstract:
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
本文详细讨论了VRML技术与其他数据访问技术相结合 ,实现对数据库实时交互的技术实现方法 ,并简要阐述了相关技术规范的语法结构和技术要求。所用技术手段安全可靠 ,具有良好的实际应用表现 ,便于系统移植。
Abstract:
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
本文介绍了作者近年来应用工具软件NU清除磁盘引导区和硬盘主引导区病毒、修复引导区损坏磁盘的 经验，经实践检验，简便有效。
Abstract:
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
本文简要介绍了海关EDI自动化通关系统的定义概况及重要意义，对该EDI应用系统下的业务运作模式所涉及的法律问题，采用EDIFACT国际标准问题、网络与软件技术问题，以及工程管理问题进行了结合实际的分析。
2016,25(8):1-7, DOI: 10.15888/j.cnki.csa.005283
Abstract:
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
从2006年开始，深度神经网络在图像/语音识别、自动驾驶等大数据处理和人工智能领域中都取得了巨大成功，其中无监督学习方法作为深度神经网络中的预训练方法为深度神经网络的成功起到了非常重要的作用. 为此，对深度学习中的无监督学习方法进行了介绍和分析，主要总结了两类常用的无监督学习方法，即确定型的自编码方法和基于概率型受限玻尔兹曼机的对比散度等学习方法，并介绍了这两类方法在深度学习系统中的应用，最后对无监督学习面临的问题和挑战进行了总结和展望.
Abstract:
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
在研究了目前主流的视频转码方案基础上,提出了一种分布式转码系统。系统采用HDFS(HadoopDistributed File System)进行视频存储,利用MapReduce 思想和FFMPEG 进行分布式转码。详细讨论了视频分布式存储时的分段策略,以及分段大小对存取时间的影响。同时,定义了视频存储和转换的元数据格式。提出了基于MapReduce 编程框架的分布式转码方案,即Mapper 端进行转码和Reducer 端进行视频合并。实验数据显示了转码时间随视频分段大小和转码机器数量不同而变化的趋势。结
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
Abstract:
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
用较少的颜色来表示较大的色彩空间一直是人们研究的课题，本文详细讨论了半色调技术和抖动技术，并将它们扩展到实用的真彩色空间来讨论，并给出了实现的算法。
Abstract:
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
本文结合物流遗留系统的实际安全状态,分析了面向对象的编程思想在横切关注点和核心关注点处理上的不足,指出面向方面的编程思想解决方案对系统进行分离关注点处理的优势,并对面向方面的编程的一种具体实现AspectJ进行分析,提出了一种依据AspectJ对遗留物流系统进行IC卡安全进化的方法.
Abstract:
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
开放平台的核心问题是用户验证和授权问题,OAuth 是目前国际通用的授权方式,它的特点是不需要用户在第三方应用输入用户名及密码,就可以申请访问该用户的受保护资源。OAuth 最新版本是OAuth2.0,其认证与授权的流程更简单、更安全。研究了OAuth2.0 的工作原理,分析了刷新访问令牌的工作流程,并给出了OAuth2.0 服务器端的设计方案和具体的应用实例。
Abstract:
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
针对智能家居、环境监测等的实际要求，设计了一种远距离通讯的无线传感器节点。该系统采用集射频与控制器于一体的第二代片上系统CC2530 为核心模块，外接CC2591 射频前端功放模块；软件上基于ZigBee2006 协议栈，在ZStack 通用模块基础上实现应用层各项功能。介绍了基于ZigBee 协议构建无线数据采集网络，给出了传感器节点、协调器节点的硬件设计原理图及软件流程图。实验证明节点性能良好、通讯可靠,通讯距离较TI 第一代产品有明显增大。
Abstract:
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
本文介绍了车辆监控系统的组成,研究了如何应用Rockwell GPS OEM板和WISMOQUIKQ2406B模块进行移动单元的软硬件设计,以及监控中心 GIS软件的设计.重点介绍嵌入TCP/IP协议处理的Q2406B模块如何通过AT指令接入Internet以及如何和监控中心传输TCP数据.
Abstract:
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
随着面向对象软件开发技术的广泛应用和软件测试自动化的要求，基于模型的软件测试逐渐得到了软件开发人员和软件测试人员的认可和接受。基于模型的软件测试是软件编码阶段的主要测试方法之一，具有测试效率高、排除逻辑复杂故障测试效果好等特点。但是误报、漏报和故障机理有待进一步研究。对主要的测试模型进行了分析和分类，同时，对故障密度等参数进行了初步的分析；最后，提出了一种基于模型的软件测试流程。
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
Abstract:
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
本文介绍了一个企业信息门户中单点登录系统的设计与实现。系统实现了一个基于Java EE架构的结合凭证加密和Web Services的单点登录系统，对门户用户进行统一认证和访问控制。论文详细阐述了该系统的总体结构、设计思想、工作原理和具体实现方案，目前系统已在部分省市的广电行业信息门户平台中得到了良好的应用。
Abstract:
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
本文介绍了Visual C++6.0在对话框的多个文本框之间,通过回车键转移焦点的几种方法,并提出了一个改进方法.
Abstract:
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
介绍了一种基于DWGDirectX在不依赖于AutoCAD平台的情况下实现DWG文件的显示、操作、添加的简单的实体的方法，并对该方法进行了分析和实现。
Abstract:
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
DICOM 是医学图像存储和传输的国际标准，DCMTK 是免费开源的针对DICOM 标准的开发包。解读DICOM 文件格式并解决DICOM 医学图像显示问题是医学图像处理的基础，对医学影像技术的研究具有重要意义。解读了DICOM 文件格式并介绍了调窗处理的原理，利用VC++和DCMTK 实现医学图像显示和调窗功能。
Abstract:
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
综合考虑基于构件组装技术的虚拟实验室的系统需求，分析了工作流驱动的动态虚拟实验室的业务处理模型，介绍了轻量级J2EE框架(SSH)与工作流系统(Shark和JaWE)的集成模型，提出了一种轻量级J2EE框架下工作流驱动的动态虚拟实验室的设计和实现方法，给出了虚拟实验项目的实现机制、数据流和控制流的管理方法，以及实验流程的动态组装方法，最后，以应用实例说明了本文方法的有效性。
Abstract:
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
本文介绍了一种将DTD转换成ER图,并用XMLApplication将ER图描述成转换标准,然后根据该转换标准将XML文档转换为关系模型的方法.
-
全文下载排行(总排行，年度排行，各期排行)
摘要点击排行(总排行，年度排行，各期排行)
Article Search
Search by issue
Select AllDeselectExport
Display Method:
Abstract:
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
论文对HDF数据格式和函数库进行研究,重点以栅格图像为例,详细论述如何利用VC++.net和VC#.net对光栅数据进行读取与处理,然后根据所得到的象素矩阵用描点法显示图像.论文是以国家气象中心开发Micaps3.0(气象信息综合分析处理系统)的课题研究为背景的.
Abstract:
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
本文介绍非实时操作系统Windows 2000下,利用VisualC++6.0开发实时数据采集的方法.所用到的数据采集卡是研华的PCL-818L.借助数据采集卡PCL-818L的DLLs中的API函数,提出三种实现高速实时数据采集的方法及优缺点.
Abstract:
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
排序是计算机程序设计中一种重要操作，本文论述了C语言中快速排序算法的改进，即快速排序与直接插入排序算法相结合的实现过程。在C语言程序设计中，实现大量的内部排序应用时，所寻求的目的就是找到一个简单、有效、快捷的算法。本文着重阐述快速排序的改进与提高过程，从基本的性能特征到基本的算法改进，通过不断的分析，实验，最后得出最佳的改进算法。
Abstract:
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
随着Internet的迅速发展，网络资源越来越丰富，人们如何从网络上抽取信息也变得至关重要，尤其是占网络资源80%的Deep Web信息检索更是人们应该倍加关注的难点问题。为了更好的研究Deep Web爬虫技术，本文对有关Deep Web爬虫的内容进行了全面、详细地介绍。首先对Deep Web爬虫的定义及研究目标进行了阐述，接着介绍了近年来国内外关于Deep Web爬虫的研究进展，并对其加以分析。在此基础上展望了Deep Web爬虫的研究趋势，为下一步的研究奠定了基础。
2023,32(4):1-15, DOI: 10.15888/j.cnki.csa.009038
Abstract:
2023,32(4):16-41, DOI: 10.15888/j.cnki.csa.009037
Abstract:
2023,32(3):25-47, DOI: 10.15888/j.cnki.csa.008980
Abstract:
2023,32(3):1-14, DOI: 10.15888/j.cnki.csa.008976
Abstract:
2023,32(2):1-12, DOI: 10.15888/j.cnki.csa.008743
Abstract:
2023,32(1):12-28, DOI: 10.15888/j.cnki.csa.008914
Abstract:
2023,32(1):29-40, DOI: 10.15888/j.cnki.csa.008909
Abstract:
2023,32(1):1-11, DOI: 10.15888/j.cnki.csa.008848
Abstract:
2023,32(1):41-49, DOI: 10.15888/j.cnki.csa.008805
Abstract:
2022,31(12):20-28, DOI: 10.15888/j.cnki.csa.008838
Abstract:
2022,31(12):1-9, DOI: 10.15888/j.cnki.csa.008825
Abstract:
2022,31(11):49-59, DOI: 10.15888/j.cnki.csa.008847
Abstract:
2022,31(11):31-48, DOI: 10.15888/j.cnki.csa.008811
Abstract:
2022,31(10):44-50, DOI: 10.15888/j.cnki.csa.008776
Abstract:
2022,31(10):25-35, DOI: 10.15888/j.cnki.csa.008734
Abstract:
2022,31(10):36-43, DOI: 10.15888/j.cnki.csa.008711
Abstract:
2022,31(9):57-69, DOI: 10.15888/j.cnki.csa.008661
Abstract:
2022,31(8):1-16, DOI: 10.15888/j.cnki.csa.008713
Abstract:
2022,31(7):12-22, DOI: 10.15888/j.cnki.csa.008641
Abstract:
2022,31(7):35-45, DOI: 10.15888/j.cnki.csa.008595
Abstract:
2022,31(7):46-54, DOI: 10.15888/j.cnki.csa.008590
Abstract:
2022,31(7):23-34, DOI: 10.15888/j.cnki.csa.008566
Abstract:
2022,31(7):1-11, DOI: 10.15888/j.cnki.csa.008545
Abstract:
2022,31(5):40-51, DOI: 10.15888/j.cnki.csa.008476
Abstract:
2022,31(5):1-20, DOI: 10.15888/j.cnki.csa.008463
Abstract:
2022,31(5):21-29, DOI: 10.15888/j.cnki.csa.008426
Abstract:
2022,31(4):14-32, DOI: 10.15888/j.cnki.csa.008419
Abstract:
2022,31(4):1-13, DOI: 10.15888/j.cnki.csa.008418
Abstract:
2022,31(4):47-58, DOI: 10.15888/j.cnki.csa.008403
Abstract:
2022,31(3):1-8, DOI: 10.15888/j.cnki.csa.008415
Abstract:
2022,31(3):48-55, DOI: 10.15888/j.cnki.csa.008377
Abstract:
2022,31(2):13-21, DOI: 10.15888/j.cnki.csa.008319
Abstract:
2022,31(2):1-12, DOI: 10.15888/j.cnki.csa.008303
Abstract:
2022,31(1):1-10, DOI: 10.15888/j.cnki.csa.008323
Abstract:
2021,30(12):28-36, DOI: 10.15888/j.cnki.csa.008289
Abstract:
2021,30(12):10-17, DOI: 10.15888/j.cnki.csa.008252
Abstract:
2021,30(12):1-9, DOI: 10.15888/j.cnki.csa.008245
Abstract:
2021,30(10):21-30, DOI: 10.15888/j.cnki.csa.008137
Abstract:
2021,30(9):12-23, DOI: 10.15888/j.cnki.csa.008086
Abstract:
2021,30(9):24-40, DOI: 10.15888/j.cnki.csa.008079
Abstract:
2021,30(9):41-52, DOI: 10.15888/j.cnki.csa.008078
Abstract:
2021,30(8):14-21, DOI: 10.15888/j.cnki.csa.008003
Abstract:
2021,30(7):13-21, DOI: 10.15888/j.cnki.csa.007992
Abstract:
2021,30(7):1-12, DOI: 10.15888/j.cnki.csa.007973
Abstract:
2021,30(6):18-27, DOI: 10.15888/j.cnki.csa.007956
Abstract:
2021,30(5):21-30, DOI: 10.15888/j.cnki.csa.007916
Abstract:
2021,30(3):1-13, DOI: 10.15888/j.cnki.csa.007839
Abstract:
2021,30(1):10-18, DOI: 10.15888/j.cnki.csa.007776
Abstract:
2020,29(12):13-25, DOI: 10.15888/j.cnki.csa.007701
Abstract:
2020,29(11):29-39, DOI: 10.15888/j.cnki.csa.007705
Abstract:
2020,29(9):16-25, DOI: 10.15888/j.cnki.csa.007632
Abstract:
2020,29(9):26-31, DOI: 10.15888/j.cnki.csa.007617
Abstract:
2020,29(9):1-15, DOI: 10.15888/j.cnki.csa.007489
Abstract:
2020,29(8):1-8, DOI: 10.15888/j.cnki.csa.007478
Abstract:
2020,29(6):56-63, DOI: 10.15888/j.cnki.csa.007460
Abstract:
2020,29(6):1-12, DOI: 10.15888/j.cnki.csa.007431
Abstract:
2020,29(6):13-21, DOI: 10.15888/j.cnki.csa.007430
Abstract:
2020,29(3):29-38, DOI: 10.15888/j.cnki.csa.007315
Abstract:
2020,29(3):1-10, DOI: 10.15888/j.cnki.csa.007301
Abstract:
2020,29(3):11-19, DOI: 10.15888/j.cnki.csa.007261
Abstract:
2020,29(2):1-8, DOI: 10.15888/j.cnki.csa.007275
Abstract:
2020,29(2):9-27, DOI: 10.15888/j.cnki.csa.007262
Abstract:
2020,29(1):14-21, DOI: 10.15888/j.cnki.csa.007243
Abstract:
2020,29(1):22-28, DOI: 10.15888/j.cnki.csa.007237
Abstract:
2019,28(12):1-8, DOI: 10.15888/j.cnki.csa.007195
Abstract:
2019,28(11):1-9, DOI: 10.15888/j.cnki.csa.007156
Abstract:
2019,28(9):1-8, DOI: 10.15888/j.cnki.csa.007037
Abstract:
2019,28(7):1-8, DOI: 10.15888/j.cnki.csa.006988
Abstract:
2019,28(6):1-12, DOI: 10.15888/j.cnki.csa.006915
Abstract:
2019,28(4):205-211, DOI: 10.15888/j.cnki.csa.006739
Abstract:
2019,28(3):1-9, DOI: 10.15888/j.cnki.csa.006812
Abstract:
2019,28(3):10-17, DOI: 10.15888/j.cnki.csa.006806
Abstract:
2019,28(1):1-9, DOI: 10.15888/j.cnki.csa.006720
Abstract:
2019,28(1):10-16, DOI: 10.15888/j.cnki.csa.006707
Abstract:
2018,27(10):39-45, DOI: 10.15888/j.cnki.csa.006591
Abstract:
2018,27(10):33-38, DOI: 10.15888/j.cnki.csa.006560
Abstract:
2018,27(9):47-51, DOI: 10.15888/j.cnki.csa.006542
Abstract:
2018,27(8):10-18, DOI: 10.15888/j.cnki.csa.006456
Abstract:
2018,27(7):1-10, DOI: 10.15888/j.cnki.csa.006451
Abstract:
2018,27(5):1-9, DOI: 10.15888/j.cnki.csa.006336
Abstract:
2018,27(4):1-9, DOI: 10.15888/j.cnki.csa.006273
Abstract:
2018,27(1):1-9, DOI: 10.15888/j.cnki.csa.006169
Abstract:
2017,26(12):32-36, DOI: 10.15888/j.cnki.csa.006129
Abstract:
2017,26(12):25-31, DOI: 10.15888/j.cnki.csa.006096
Abstract:
2017,26(6):36-39, DOI: 10.15888/j.cnki.csa.005807
Abstract:
2017,26(4):1-8, DOI: 10.15888/j.cnki.csa.005773
Abstract:
2017,26(2):30-36, DOI: 10.15888/j.cnki.csa.005626
Abstract:
2016,25(9):17-26, DOI: 10.15888/j.cnki.csa.005354
Abstract:
2016,25(9):10-16, DOI: 10.15888/j.cnki.csa.005330
Abstract:
2016,25(8):1-7, DOI: 10.15888/j.cnki.csa.005283
Abstract:
2016,25(6):9-16, DOI: 10.15888/j.cnki.csa.005173
Abstract:
2016,25(6):1-8, DOI: 10.15888/j.cnki.csa.005206
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
Abstract:
External Links
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address：4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code：100190
Phone：010-62661041 Fax： Email：csa (a) iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address：4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code：100190
Phone：010-62661041 Fax： Email：csa (a) iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
